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10075476 Tom Minor Narender Ramnani MODEL FREE APPROACHES TO INVESTIGATING THE NEURAL MECHANISMS OF WORKING MEMORY


ABSTRACT A re-analysis of data from a study into the cerebellar contributions to working memory (Hayter et al., 2007), was conducted, using the same General Linear Model design matrix and an extension using Independent Component Analysis allowed for a comparison of model-driven and data-driven statistical approaches and their contributions to understanding the neural dynamics recruited during a verbal working memory (VWM) task (PASAT). The increase in cognitive load within the ADD condition, and the resulting changes in BOLD response was operationalised as the variable most likely to demonstrate the execution of integrated cognitive control. Focussing on brain areas that were found to be significantly activated during the ADD > REPEAT condition, an attempt to evaluate model-free approaches to neuroimaging is made and the suggestion that the use of both data-led and hypothesis-driven models, may best suit an area of science that is renowned for the difficulty in reliably interpreting incredibly noisy and complex data-sets, as in functional Magnetic resonance imaging (fMRI)


INTRODUCTION D’Esposito (2007) defines Working Memory (WM) as the temporary retention of information recently experienced or retrieved from long-term memory that no longer exists in the external environment. A good test of WM is the Paced Auditory Serial Addition Task (PASAT) because it enables researchers to manipulate cognitive and sensory demands within a task that can be learned and executed within neuroimaging studies and allows for functional and anatomical introspection into the inner workings of WM at the level of the brain. Evidence related to the neural substrates of WM will be considered before introducing 2 different statistical approaches, as the case for either model-based or data-driven methodologies to interpreting fMRI, data from a WM task is assessed. Neuroscientific data, from primate studies will be discussed to generalise and further our appreciation of the range of brain areas that appear to be related to WM in humans .

Smith et al., (1996), confirms that the posterior parietal cortex is implemented lefthemispherically, as a neural storage component to VWM, including a sub vocal rehearsal component in Broca’s area and premotor and supplementary motor (frontal) areas. Smith, (1998) also concludes that there is scientific convergence on the idea that there are separate systems for verbal and spatial memory, in support for Baddeley’s decomposition of VWM into a phonological buffer and rehearsal process


within the left posterior parietal cortex (Brodmanns areas/BA40), Broca’s area (BA44), left premotor area (BA6) and left supplementary motor areas (BA6), which are found to be activated in the maintenance and manipulation of information in VWM. Broca’s aphasics show a steeper forgetting curve than controls (Korsakoff’s patients), suggesting that this poorer performance is likely to be caused by a lack of subvocal rehearsal, also left posterior parietal cortex lesions seem to interrupt verbal repetition, implying a deficit in the storage component of VWM and offering support to the idea that the posterior parietal area is generally involved in phonological processing and not restricted to memorisation (Smith et al., 1996).

Majerus et al., (2006) note that the intraparietal sulcus (IPS) is one of the most consistently activated regions during verbal short-term memory (VSTM) tasks, although the precise role it plays is a matter of controversy. IPS activity was found in Hayter et al., (2007), and the current reanalysis, providing a motivation to model and try to theoretically understand why this region (and many others circumventing the IPS) show significant activation in this experiment and what that might tell us about the functional connectivity of potential networks of structurally independent but functionally integrated brain regions involved in VWM. The IPS is functionally connected to serial/temporal order processing areas in the right IPS, premotor and cerebellar cortices (Majerus et al., 2006), suggesting some functional interhemispheric interaction and strengthening the case that bilateral IPS activity may be related to the cognitive demands of the task. During STM tasks, the left IPS shows connectivity to the phonological, and orthographic processing areas in the superior


temporal and fusiform gyri (Marjerus et al., 2006), suggesting that the IPS acts as an attentional modulation area for distant neural networks specialised in language or order representation and strengthening attention-based accounts of VSTM.

Chen and Desmond, (2005) have implicated inferior and premotor frontal cortex, the insula and the cerebellum in VSTM tasks, demonstrating that areas outside of the parietal lobe are also involved in processes related to the phonological store and providing evidence for a fronto-parietal-cerebellar network for VSTM. These researchers also found that the IPS is connected to the medial superior frontal areas, the left cuneus, and the posterior cingulate extending to the precuneus. Again, these results reflect the original findings from the Hayter et al., (2007) study and seem to be best represented in the current results from the extension analysis with a model-free approach as opposed to the model-driven approach. Due to a significant part of the operated experiment being verbal instruction and potential subvocal rehearsal, it seems poignant to include the IPS and its connectivity, focussing on its contribution to an integrated network of neural dynamics, evidenced to underlie VSTM. The first statistical approach used was a General Linear Model (GLM), which assumes Gaussian distribution of data. GLM makes this assumption and sometimes this rigid hypothesis is wrong, leading to inaccurate data that misses something with import for the experiment, because it was not modelled (expected) in the design matrix and therefore considered as noise. Hemodynamic activity varies across many factors, including intra-individually, GLM assumes that all data represents a Gaussian distribution, and ignores any data that isn’t distributed in a Gaussian manner, as noise.


Not all hemodynamic activity related to the experiment is Gaussian in nature; there may be unexpected, non-Gaussian distributed data that still has something to tell us about the processes of WM and this is why the use of data-driven approaches is required. Using a General Linear Model (GLM) statistical approach to their data analysis, Hayter et al., (2007), defined the parameters of their design matrix, according to the variables of interest (regressors) and considered all activity related to the temporal dynamics of their experiment, thus interpreting results based on effects due to voxel-wise interactions that could be accounted for by their preconceived model, driving the analysis in a hypothesis led fashion. In order to test the usefulness of GLM, a second statistical approach was introduced, this was Independent Component Analysis (ICA). ICA has its own problems in that, without a defining model to refer to, there are interpretive limitations. Fortunately a new form of ICA, used in this study, allows for the analysis of regressors, so that the data set represents the best of model–free and the best of model-driven statistical methods in order to more closely scrutinize results. ICA allows for the blind source separation of a mixed data-set into its component parts, dealing with ‘functional segregation’ by detecting focal differences in neuropsychological effects in terms of a number of regionally specific changes whose significance can be assessed independently of changes going on elsewhere in the brain (Friston, 1998). ICA is a data-led approach to the analysis of fMRI data, allowing for exploratory research. fMRI data sets, represent a mixture of signals resulting from hemodynamic changes, reflecting neural activity, motion and machine artefacts and signals from physiological cardiac and respiratory pulsations: the relative


contribution of each of these components is undetermined, suggesting a role for blind source separation (Jung et al., 2000). In dealing with the ‘neural cocktail-party problem,’ ICA allows for the delineation of a noisy signal into its important component parts, attempting ‘true redundancy reduction’ by ‘minimalising mutual information,’ though it is important to note that true ‘independence’ does not really exist because neurons can be coupled by synapses and share efferent connections (Brown et al., 2001). ICA is particularly effective at detecting task-related activations, including unexpected activity that would not be detected under the premise of a model-based analysis, which considers unexpected activity as error. It is possible to input a regression framework into the ICA, allowing for a combination of hypothesis testing and data-driven research (Brown et al., 2001).

McKeown et al., (1997) state that the principle of localisation implies that each psychomotor function is performed in a small set of brain areas, which are anatomically segregated for each function. They point out that the goal for decomposition of fMRI data into physiological and cognitive components is to determine topographically distinct brain areas that are co-activated during timeseries acquisition throughout the experiment. Finally, under McKeown’s conception of ICA, it is equivalent to saying that voxel values in one component, convey nothing of any value about the voxels in any other component, which is a stronger criterion than the assumption of GLM (that maps of voxel-wise interactions from different components are uncorrelated and Gaussian in nature). In attempting to assess the contribution of cortico-cerebellar loops in the execution of


skilled cognitive operations, Hayter et al., (2007) reported activity in the medial portions of cerebellar cortical lobule VII, linking these activations to the motor demands of their experimental condition (ADD) within the PASAT. Noting the substantial evidence of prefrontal-cerebellar connectivity (Hoover & Strick, 1999, Kelly & Strick, 2003, Middleton & Strick, 2001; Walker, 1940), Hayter et al., (2007) tested the hypothesis that highly practiced execution of action, engages cerebellar circuits (Wolpert et al., 1998) by manipulating the sensory demands within a task that required speech motor control. Activity was predicted in the auditory areas of the superior temporal gyrus and ventral areas of the Primary Motor Cortex (PMC/M1) -containing orofacial musculature representations, along with cerebellar lobules, known to be components of the motor loop (IV, V and VI). In addition to confirming their hypothesis, Hayter et al., (2007), found co-activations in mid portions of the middle frontal gyrus (9/46 putatively), anterior portions of the cingulate cortex and areas within the pars triangularis (inferior and middle frontal gyri/Broca’s area), supporting literature documenting the activation of these areas during WM in humans (Curtis & D‘Esposito, 2003; Passingham & Sakai, 2004; Smith & Jonides. 1996). The Hayter et al., (2007) study provides support for the ‘automaticity function’ of the cerebellum by demonstrating that the cerebellum is involved in the execution of learned actions thus freeing up cortical structures to deal with novel information processing. It is proposed that due to the uniform cytoarchitecture of the cerebellum, considered alongside the consistent findings of activity related to the control of motor speech production and prefrontal cognition, that the mechanisms supporting automaticity of action and automatic information processing may be similar


(Ramnani 2006). Hayter et al., (2007) add to the consistently robust finding of activations in the lateral convexity of the PFC in relation to maintenance and manipulation-related demands of WM (Curits & D’esposito, 2003; Passingham & Sakai, 2004), highlighting the role of the cerebellum in processes linked to verbal WM and emphasising the use of these processes in acquiring a skill through the process of automaticity.

Cerebellar contributions to WM form the starting point of this investigation. Using the rationale and data from the Hayter et al., (2007) paper, this investigation is based on discovering what impact model based, hypothesis-driven techniques as compared to minimal assumptions, data-led approaches to analysing fMRI data, have on the actual statistical processing and interpretation of results. As such, part of this investigation is a re-analysis of the Hayter et al., (2007) data set, using the same design matrix within a GLM analysis (though on a later SPM5 program) and a comparison to an extended analysis, using ICA, including a GLM model-fit, displaying the variance of results based on the 2 techniques. On the whole, results from the re-run, corresponded to the detail in the Hayter et al., (2007) study, some differences were found and these will be discussed in terms of the operating systems and potential human error, within a broader discussion of the techniques used and potential fronto-parietal-cerebellar network for VSTM. The further analysis (ICA) was able to allow for potentially more detailed, and more selective choice of peak-voxel activation and as such, brings the possibility of a more complex view of the structure and function of the hemodynamic activity relative to the original experiment.


The reliability of neuroscientific interpretations of physiological data sets is intimately linked to the quality of the scientific tools and methods used in detecting and localising brain activity, which relies on a less than uniform nomenclature and differing sets of anatomical atlases. The search for detailed knowledge of how the brain works, and how interconnected but distinct brain areas communicate with one another; through which pathways and in what serial order, has been greatly advanced using non-human primate research which allows for deeper introspection into the mechanics and architectures of brain areas that are typically out of reach in human subjects, due to their critical, sub-cortical or frontopolar location.

Petrides & Pandya., (2001) have done much to assist in establishing meaningful comparisons between the human and macaque brain by developing a common architectonic map, allowing for delineations based on functionally and structurally evolved parts of the human PFC and also contributing to our understanding of brain function. Comparing the cytoarchitectonic nature of the human and macaque ventrolateral PFC (vlPFC) and corticocortical connection patterns in the monkey, via injecting tracers into Walkers area 12, herewith referred to as area 47/12. The multimodal input into this area can be assessed and fed into a model of how this area differs in its function to the corresponding brain area in humans, and what it is about its function in monkeys that can add to our ‘as-close-to’ complete view. Tracers were found in the rostral inferotemporal visual association cortex and in temporal limbic areas such as the parahippocampal cortex. Dorsally adjacent to 47/12, lies area 45


which represents the dorsal part of the vlPF convexity, just below area 9/46 and caudodorsal to Brodmanns area 47. Injections to area 45, labelled the Superior Temporal Gyrus (STG) (auditory association cortex) and the multimodal cortex in the upper bank of the Superior Temporal Sulcus (STS). Area 45 occupies a large part of the pars traingularis. Tracers beginning their journey here were also found in dorsolateral frontal areas 6, 8Ad, 8B and 10, as well as area 24 of the cingulate gyrus. Petrides & Pandya (2001), suggest that area 45 subserves higher-order aspects of organisation of linguistic processing, but is not limited to that; due to the existence of these areas in monkey, the implication of a more general and fundamental role in cognition which may have adapted in humans to serve linguistic processing in the left hemisphere, is posited. Petrides, (2005) believes that ventrolateral prefrontal cognition controls information processing in posterior cortical areas necessary for active retrieval of information from memory. Area 47/12, with its strong links to rostral inferotemporal visual association cortex and ventral limbic areas (rostral parahippocampal gyrus) may be a part of a frontal mid-ventrolateral exectutive system that is involved in active judgements on stimuli that are coded and held in posterior association cortex (Petrides, 2005). This body of work, does much to illuminate some of the findings from the current re-analysis of the Hayter et al., (2007) data, using an ICA, as all the areas mentioned, show activation and show tentative support for these hypotheses (frontal-midventrolateral system). Fuster (2004), refers to Koechlin et als., (2007) hierarchical cascading model of executive processes in the lateral cortex of the frontal lobe. Discussing the upper processing stages of the perception-action cycle, he concludes that at all levels of the


CNS, sensory processing happens sequentially and along a posteroanterior axis, with feedback at every level. Cortically, information is believed to flow circularly through a series of hierarchically organised areas and connections constituting the perceptionaction cycle. Within this conception, automaticity (cerebellar learning in order to free up frontal areas for novel information processing) is implied. Koechlin et al., (2007), view the lateral PFC (lPFC) as a cascade of executive processes, organised from premotor to anterior PFC regions, that control behaviour according to the stimulus, perceptual context and temporal dynamics of the stimulus, providing a unified and modular account of cognitive control. This top-down interaction between the lPFC regions and premotor or posterior association cortices predicts that episodic, contextual and sensory contributions to cognition, gradually emerge from rostral to caudal lPFC and premotor regions, and that the increasing demands of these factors have additive effects of behavioural reaction times, this is characterised by Weber’s Law, (Blake & Sekuler, 2006). On the basis of this research, it is believed that rostral activations of the lPFC (inferior/middle frontal gyrus; pars triangularis) will result from variations in WM load, (maintaining instructions relative to cues over the course of experimental episodes) and that these areas are engaged in selection of appropriate representation-for-action modules, processed across episodes and exert control on the caudal lPFC, but not directly on the premotor representations – it is the caudal regions that select premotor responses to task sets according to the context, and rostral regions select caudal lPFC representations, modelling the classical theory of executive control based on central control of multiple slave systems (Koechlin et al., 2007)


The caudate nucleus seems to function similarly to the PFC, in that damage to both areas result in similar deficits due to the sharing of inputs from frontal and temporoparietal cortical areas and outputs to the basal ganglia. Part of the fronto-caudate pathway projects via the thalamus back to BA 46 in PFC, suggesting that the cognitive functions of the dorsolateral prefrontal (dlPF) and anterior cingulate (AC) pathways implement the caudate (Abdullaev et al., (1997). The caudate (tail & head) is activated in Hayter et al., (2007) and in the current re-analysis, including the GLM and ICA, these results fits with cascade model along the posteroanterior axis for corticosubcortical WM operations.

Desikan et al., (2006) warn that there is limited ability to qualify critical dimensions of interest in localisation of fMRI data, because of the considerable inter-individual variability of topographic cortical features. Stating that the banks of the superior temporal gyrus (STG) and pericalcarine cortex have larger interindividual variability due to their pure sulcal nature. Bearing this mind, a comparison of results from the original data set and the current reanalysis with the same GLM on a newer SPM5 programme and a new version of ICA that allows for GLM regression analysis and illustrates the variations in results between the two approaches, allowing for some good comparisons and elucidating the neural mechanics of VWM in the PASAT.

METHODS This is a report from an already published data set, we report the salient issues.


PARTICIPANTS:

The original study involved 15 Right-handed volunteers between the ages of 18 and 29 (6 males) who gave written and informed consent confirming that they had no neurological or psychiatric history. The study ran in line with the Royal Holloway University of London (RHUL) MRI Rules of Operation and the Medical Devices Agency. Ethical approval from the RHUL Psychology Department Ethics Committee was received for a reanalysis of the original results and no experiment took place.

EXPERIMENTAL DESIGN:

The aim of this study was to investigate what differences were found in the results of a reanalysis of the original data from the Hayter et al., (2007) paper, using the same General Linear Model used in the original study and an Independent Components Analysis.

Paced Auditory Serial Addition Test (PASAT):

Tombaugh (2005) recognises that the PASAT measures the performance of subjects across multiple sensory modalities due to the requirement of successful completion of a variety for functions related to attention, including audition, visual perception, numerical cognition and speech. In the original study, the adaptation of the PASAT


allowed for the investigation of BOLD activity related to the specific cognitive demands of the task, whilst enforcing strict experimental controls. The preparation phase, outside of the scanner allowed for familiarisation with requirements, then subjects practiced the test inside the scanner, to familiarise themselves with the test environment. The experimental phase consisted of 35 blocks of experimental (ADD), 35 block of control (REPEAT), and 10 null blocks (with no stimuli), lasting 32 minutes altogether. Blocks were pseudo-randomly intermixed. For more detail about the PASAT test, refer to Hayter et al., (2007)

APPARATUS:

Participants lay supine within the MRI Scanner with padded restraints immobilising their head. Instructions were received audibly through headphones that were compatible with MRI apparatus. Verbal responses were measured using an MRIcompatible microphone. An overhead mirror mounted on a head coil made the visually presented instructions viewable as they were back projected onto a screen. Experimental and control stimuli were controlled by a computer. For detail and timings of experimental episodes refer to Hayter et al., (2007).

IMAGE ACQUISITION:

Participants were scanned with the 3-T Siemans Trio MRI scanner at RHUL. First structural images were acquired. The functional sequence during the experimental


phase was made up of 644 EPI images and 4 volumes were collected before the experiment began to minimise longitudinal relaxation artefacts (Hayter et al, 2007).

SPARSE SAMPLING:

Participants produced overt verbal responses during periods of deliberate scanner silence (sparse-sampling) in order to allow for and cope with the head motion artefact expected during the spoken response.

MRI Image Analysis:

Image processing and analysis for the original replication were carried out in SPM5 (www.fil.ion.ucl.uk/spm) on a dual core AMD Athlon 64 MHz PC with 2 GB of RAM Linux server 2003 and Matlab 6.5 (MathWorks.Inc., MA, USA)

For the ICA, the GLM from the previous analysis was fed into the ICA via the FSL programme on the same PC and server using Linux.

Preprocessing:

REALIGNMENT Realignment was computed with reference to the first EPI time-series and the resulting head motion parameters (3 rotations and 3 translations) were saved and included as


regressors within the GLM. This step attempts to model the acquired head motion regressors, statistically, to allow for accuracy in later steps based on the variance in results that may be caused by the subjects translating or rotating their head during the verbal response phase of the experiment and thus influencing the maps of activity.

NORMALISATION

Brains aren’t all the same, so in order to meaningfully interpret the results, subjects’ individual brains need to be adjusted statistically to reduce individual differences and provide a common stereotaxic space from within which to compare localised activations across the board. Normalising the volumes to the ICBM template (Montreal Neurological Institute series; MNI) of standard stereotaxic space was achieved using rigid, linear scaling that brings the individual brain into the realm of stereotaxic space and then non-linear warping which selects particular bits of the brain that seem to have evaded normalisation through linear transformations and require particular attention in order to match the template meaningfully.

SMOOTHING

An 8mm smoothing kernel was applied at the final stage of preprocessing. Within the GLM analysis, these steps had to be processed manually, with the aid of the SPM5 programme and within FSL, for the ICA, these steps were computed automatically. Smoothing data amplifies significant signals and reduces signals of no interest (noise).


The size of the kernel is essentially an arbitrary value, however using a smaller kernel may have magnified many more subcortical activations and it would be extremely unfruitful to use higher than 10mm kernel, because at that range, segregating activations loses specificity, especially between anatomically proximal areas. Smoothing kernels need to be at least twice the size of the voxels to be able to tell us anything meaningful, otherwise they may miss minute details in signal significance, reducing something interesting or elaborating something that is in fact noise and of no interest.

Statistics:

GLM is conducted using a Linux system, Statistical Parametric Mapping (SPM) programme within MATLAB. This programme allows you to create the design matrix, defining the parameters of the variables of interest. In this case, head motion regressors (3 x rotations/ 3 x translations), conditions (instruction, add, repeat) and error were modelled within the design matrix. Preprocessing the data for SPM5 involved realigning the individual subject scans on the basis of the head motion artefact to measure the extent to which a particular scan deviates from the first timeseries. By modelling approximate estimations of head motion from fMRI data, using sparse sampling as a way to measure minute differences in head position before and after overt verbal responses are made during scanner silence, SPM5 re-slices the brain scan, adjusting the data according to the variance in signal due to motion, as a first step to creating statistical validity. Normalising individual brain images (T1s) into a


common stereotaxic space (MNI template) requires telling SPM which brain needs normalising and selecting the correct template within which to fit it. The linear affine scaling and non-linear warping of the images that fits the individual subjects brain into the template brain requires selecting the mean image from the newly realigned files as the source image, and then selecting the 644 realigned files to be normalised to an EPI image. The last step before setting up the GLM design matrix, is smoothing, which requires inputting the voxel dimensions of the isotropic smoothing kernel desired for use (8mm x 8mm x 8mm).

Event definition and modelling:

The four events modelled from the original experiment were: Instruction (1s); ADD blocks (15s); REPEAT blocks (15s); and Error blocks (15s). GLM explains how much each regressor explains the variance in the data, in order to do this GLM needs to know the temporal dynamics of the experiment so that it can identify different time points of each block and convulve them with the hemodynamic response recorded.

First-level single-subject analysis (Parameter estimation of GLM):

Within SPM5, after all preprocessing has been completed, specifying the 1 st level statistics begins by selecting the subject’s smoothed files, specifying the interscan interval as 3 seconds and manually inputting the details of the 4 conditions of the


experiment (Instruction, Add, Repeat and Error), which later allow for t-contrasts. For each condition, the Onset files have to be imported from the original data and the duration of the blocks have to be specified as 15 seconds, except for Instruction which was 1 second. A High Pass filter was set to 60 and a canonical hemodynamic response function was selected as the basis function (expected BOLD response) before the model was saved. Model setup completed the estimation stage of the GLM, at which point some checks were necessary. By selecting review in SPM5, the orthogonality of the design matrix is displayed, this allows for a quick check that there are the right number of regressors within the design matrix and that they have been estimated and modelled correctly. Once satisfied that the model is correct, the GLM analysis for 1 st level were run (See Table 3 in results for 4 individual GLM 1 st level analyses). Tcontrast comparisons were run on: (1) INSTRUCTION only; (2) ADD only; (3) REPEAT only; and (4) ADD > REPEAT.

T-contrasts and statistical parametric mapping (SPM{t}) maps highlighted voxels within MNI space where Blood Oxygenation Level Dependent (BOLD) responses were significantly different between the conditions ADD and REPEAT. The SPM.mat file that is produced from the 1st level GLM is specified within SPM5, this requires manual input of the values of different conditions, allowing SPM to bring up the brain activations that relate to the specific demands of the contrast. For example, Add greater than Repeat would be defined as Add carrying a weight of 1 and Repeat carrying a weight of zero, for the statistics to produce maps that reflect brain activity that was specific to the Add condition and greater than the activity found in the Repeat condition. At this


point, whole brain threshold tables are produced, highlighting peak-voxel clusters, and SPM5 allows overlays onto canonical templates which are a useful tool for localisation, as they share similar landmarks to the anatomy in the atlas used for localisation (Duvernoy & Bourgouin, 1999), and match the template in the MRICRO programme which allows for manual input of MNI co-ordinates in order to specify where activity is in MNI space. SPM5 also allows for overlaying back to individual T1 images, providing closer, adherence to individual variations in brain size and shape.

Second-Level between-subjects analysis:

This involves a one-sample t-test applied to the contrasts from the first level statistics (all results thresholded p<0.05, FWE corrected). Specifying 2 nd level statistics for GLM on SPM5 simply plugs the output from the 1st level statistics into the group analysis. By selecting the contrast image files, saved from the 1 st level, SPM5 computes group level analysis on the contrasts you ask it to, finding overwhelming common activations between subjects. Classical as opposed to Bayesian inference methods were chosen and results were corrected for Family Wise Error, which is a more commonly used, stringent correction for multiple comparisons, applied to each and every voxel, meaning that GLM computes 100,000 t-tests at this level. The maps produced at 2 nd level can be treated as in the 1st level, with interactive thresholds and overlays within the SPM5 programme. Since group level statistics, on the whole, tell us more about strong findings, a table of the results of the contrast of Add > Repeat is presented in Table 1 of the results. Areas of significant activation were localised using a overlay


function within the SPM5 setup; overlaying the T1 functional results (SPM{t} maps) onto a canonical anatomical representative T1 image (MNI Series). Due to the variation of landmarks within MNI space, a cross check involving overlaying individual SPM{t} maps back onto the normalised T1 scans for each subjectsâ&#x20AC;&#x2122; results from the original study was done.

Independent Component Analysis:

ICA does not require predetermined regressors in order to separate the mixed signal into its significant component parts. It is possible however to enter the GLM model into the ICA on the FSL programme, allowing for an inspection of how well ICA separates components of head motion, machine and physiological artefact from interesting neuronal activity and the relevant, task-related hemodynamic components. Within FSL, it is possible to import head motion vectors into the analysis and build them into the weak model. 4 event variables were built into the model (Instruction, Add, Repeat and Error), and prestats were computed using MCFLIRT. The smoothing kernel was set to 8, as in the GLM. Registration to MNI space, setting the high pass filter to 60 and the number of components set to 50 completes the model setup and then ICA is run on individual subjects data in MELODIC within FSL. Once the ICA has run, within FEAT-FMRI in FSL, it is possible to load the 4-dimensional EPI files for each subject and to inspect each component that ICA found that could be separated from the source signal. Checks were made as to the periodicity of activation within the component and whether it related to the periodicity of the experiment, using temporal


timecourses (see results). Tables with p-values highlighted the most significant contrast within each component, allowed for relation to specific contrasts such as Add > Repeat. Gamma distribution graphs (see results) also helped to demonstrate that ICA was indeed delineating Gaussian distributed data (components) from a mixed nonGaussian source. ICA produces many images for each component, mapping brain activity in different slices of the brain, from the cortex down to the cerebellum. It also produces maps of relative deactivation and colours these differently to activations, providing interactive thresholds at every level. It is not possible, however to overlay the images produced from the results of ICA within FSL, onto the individual TI brains, or into a canonical template MNI space, this makes localising activations, more vulnerable to human error. Duvernoy & Bourgouin (1999) was the anatomical atlas used in both the GLM and ICA investigation.

Localising activation for both analyses, involved choosing sagittal and axial planes of view of the brain within the atlas and finding landmarks that reflected the dorsoventral and medial-lateral position of the images within the program. It was important to use at least 2 planes of view, in order to make sure, something than looked cortical, was not in fact subcortical and vice-a-versa. Unfortunately, the atlas carries a tremendous amount more detail than either SPM5 of FSL seem capable of producing at this stage, and even with the assistance of the MRICRO programme within SPM5, considerable attacks on confidence levels for the accuracy of localisation could be made. As far as this study goes, it is believed strongly that the authorâ&#x20AC;&#x2122;s localising skill is one of the more reliable aptitudes brought to this project, and much


painstaking time was spent, to ensure that the brain area selected as representative of the result, was indeed, the closest possible result that could be reported. Clearly, there were times when the activity could not be definitely located to a marked area within the atlas, but in these cases, sulcul and gyral anatomy was used as a gross morphological attempt to localise as accurately as possible.

RESULTS Table 1: (2 level analysis of 1

level GLMs )ADD>REPEAT (FWE corrected for multiple comparisons, p<0.05, random effects analysis). nd

st

LOBE

Gyri/Sulci

MNI Co-ords

Frontal (PF/PMC) PFC

Superior Frontal Gyrus (L) Middle Frontal Gyrus (L) Cingulate Gyrus (L) Cingulate Sulcus [R] Central Gyrus/Sulcus [R] Precentral Gyrus [R]

0 12 48 -42 0 52 8 26 32 -6 -68 52 -46 -36 44 -40 -48 55

13.25 13.08 11.59 12.34 11.38 11.38

5.81 5.78 5.57 5.66 5.49 5.49

Superior Precentral sulcus[R]

-40 -48 55

11.38

5.49

Circular Insular sulcus [R] Circular Insular Sulcus (L) Anterior/Intermediate transverse Temporal Gyrus (L)

-22 24 8 38 20 -4

11.47 10.56

5.51 5.33

38 20 -4

10.56

5.33

Caudate Nucleus [R]

-18 -8 24

12.46

5.51

Subthalamic nucleus /Substantia Nigra (L) Cerebellum [R]

14 -4 -10

10.27

5.28

-6 -50 -38

12.11

5.62

PMC

Temporal

SUBCORTICAL Basal Ganglia

Stats T

Stats Z


Results were corrected for Family Wise Error, a standard conservative control, which finds activations that are above 0.05 (p-value). If activations survive FWE correction, we know that there is absolutely something happening in the voxels highlighted. FDR correction finds more reasonable activations, but it was felt these gross voxel clusters would inhibit fine-tuned localisation with certainty. Below is an example of the output in SPM5 for the group level analyses of 1 st level GLM. Whole brain interactive thresholds, to select peak activity within voxel clusters

Table 2 (ICA results of individual 1st level analysis) LOBE Frontal

Broca's area

Parietal

Occipital

Gyral/Sulcal anatomy STATISTICS z p Superior frontal gyrus z = 5.64 p < 0.0000 Middle frontal gyrus z = 5.64 p < 0.0000 Cingulate gyrus z = 0.68 p < 0.03501 Inferior frontal gyrus z = 5.64 p < 0.0000 Superior frontal sulcus z = 5.64 p < 0.0000 Medial orbital gyrus z â&#x20AC;&#x201C; 1.88 P < 0.02979

Lateralisation

Central sulcus

Bilateral

z = 4.48 p < 0.0000

Bilateral Bilateral Bilateral Bilateral Bilateral Bilateral

Inferior precentral sulcus z = 5.02 p < 0.00000 Superior precentral sulcus z = 4.48 p < 0.0000 Superior postcentral sulcus z = 6.18 p < 0.0000 Paracentral sulcus z = 6.18 p < 0.0000 Superior lingual gyrus z = 0.68 p < 0.03501

Bilateral Bilateral Bilateral Bilateral Bilateral

Supramarginal gyrus Subparietal sulcus Interparietal sulcusv Post central gyrus

z = 5.02 p < 0.00000 z = 0.68 p < 0.03501 z = 0.68 p < 0.03501 z = 4.48 p < 0.0000

Bilateral Bilateral Bilateral Bilateral

Superior parietal gyrus z = 5.64 p < 0.00000 Precuneus z = 5.02 p < 0.00000 Angular gyrus z = 5.02 p < 0.00000 Lingual sulcus z = 0.68 p < 0.03501 Inferior lingual gyrus z = 4.66 p < 0.0000 Calcarine sulcus z = 4.66 p < 0.0000 Posterior transverse Collateral Sulcus z = 6.18 p < 0.0000

Bilateral Bilateral Bilateral Bilateral Bilateral Bilateral


Temporal

Inferior temporal gryus z = 6.18 p < 0.0000 Superior temporal gyrus z = 6.18 p < 0.0000 Middle temporal gyrus z = 6.18 p < 0.0000 Superior temporal sulcus z = 6.18 p < 0.0000 Fusiform gyrus z = 3.06 p < 0.00111 Subcollosal Gyrus z = 2.53 p < 0.00570 Parahippocampal gyrus z = 5.59 p < 0.0000

Bilateral Bilateral Bilateral Bilateral Bilateral Bilateral Bilateral

Subcortical Basal ganlia

Hippocampus z = 6.18 p < 0.0000 Head of Caudate nucleus z = 2.53 p < 0.00570 Basal nucleus of amygdala z = 5.59 p < 0.0000 Crus Cerebri z = 6.18 p < 0.0000

Bilateral Bilateral Bilateral Bilateral

Some examples of the bilateral activation. Table 3. Common activations, individual comparisons of 1st level GLM and individual ICA analyses. Subject AM

Common Activation (ICA & GLM

GLM only

ICA only

lobule VII crus ii precuneus tail of caudate Superior parietal gyrus central sulcus Superior frontal sulcus

superior lingual gyrus inferior lingual gyrus inferior temporal gyrus superior temporal gyrus middle temporal gyrus superior temporal sulcus Paracentral sulcus Superior postcentral sulcus

FK

Inferior frontal sulcus Superior frontal gyrus Superior temporal gyrus

Cingulate sulcus Crus I Inferior frontal sulcus Superior frontal gyrus Cingulate gyrus

Inferior lingual gyrus head of the caudate inferior frontal gyrus superior frontal sulcus Middle frontal gyrus central sulcus precentral gyrus postcentral gyrus Precuneus

RO

Superior frontal sulcus Superior frontal gyrus Inferior frontal sulcus Inferior frontal gyrus Middle frontal gyrus Post central gyrus Lateral Fissure Angular gyrus

Lobule VII Crus II Superior precentral sulcus Supramarginal sulcus Putamen Superior precentral sulcus

Head of the hippocampus Precuneus Superior temporal sulcus Inferior precentral sulcus Supramarginal gyrus Central sulcus Precentral gyrus Superior Parietal gyrus

SD

Caudate nucleus Lateral fissure

tail of caudate Superior frontal sulcus

head of caudate Inferior frontal gyrus


Superior frontal gyrus Putamen Superior temporal sulcus

Inferior temporal gyrus Superior postcentral sulcus Lingual sulcus cingulate gyrus precuneus Inferior precental sulcus Central sulcus Subparital sulcus Superior parietal gyrus Intraparietal sulcus Angular gyrus Paracentral lobule Cingulate sulcus

DISCUSSION Seeing as the implicit goal of this thesis was to establish the methodological and interpretational effects of using model-free approaches to biomedical signalling experiments, this discussion will begin with focus on the statistics including the technical issues related to the operating systems used for each and then go on to discuss the specific anatomical discrepancies that show up in a comparison between results from a model-based GLM and model-free ICA, relating to the research introduced at the start of this paper. The Hayter et al., (2007) hypothesis focussed on testing the interconnectedness of the prefrontal cortex with the cerebellum in line with past research referred to in the introduction and they used a GLM statistical approach. This study attempting to discover what more could be said about the neural dynamics of the same data from a PASAT VWM task, using a different, model-free statistical approach (ICA).


To create a GLM, manual input of the regressors of interest into a design matrix is required, within SPM5 and MATLAB, allowing for a close inspection of the BOLD responses relevant to the experimental episodes of ADD vs REPEAT condition. Maps of peak-voxel cluster activity were produced and there were some definite similarities as well as considerable lack of matching activity between the current results of the earlier results from Hayter et al., (2007), especially in the Occipital and Parietal lobes.

These differences, became, considerably more detailed in the ICA maps, which produced nearly 600 less components for the analysis of temporally relevant activations. Due to the sheer density of human programming, it can be estimated that some of the lacking activations within the GLM re-run, were due to human error, and disparate inter-observer reliability but more importantly, the fact that the Hayter et al., (2007) study was solely driven by a hypothesis that expected to see considerable activity in the cerebellum may have lead to their finding more supra threshold voxels in that area. It is not suggested that the previous researchers, made up these findings, only that the considerable variation could be due to the inadequacies of the programs used to produce such maps and the substantial room for selectivity in honing in on particular areas within whole brain activity that support predetermined hypotheses in the minds of the researcher and within a model-based approach in general.

There are several comparable differences and difficulties regarding the localisation of activations from data from multiple subjects into a standardised space that allows for direct comparisons, aside from the issues mentioned above, both FSL and SPM5 have


their strengths and drawbacks with regards to this salient issue.

Whereas the GLM analysis within SPM5, lists tables with peak-voxel activations, allowing you to explore the variously spread clusters of activation, to find the most significantly activated voxel amongst a cluster of activations. FSL does not provide interactive thresholds; it is possible to go into the components and establish which activation was â&#x20AC;&#x2DC;peakâ&#x20AC;&#x2122;, but as for a simple method for doing that, there seems not to be none, other than the colour intensity bars produced aside the component images, reflecting the significance of the signal, using the colour spectrum as representative, but it is extremely difficult to see, clearly, which areas are most significantly activated and to get a sense of the relative activity across the brain; essentially its like trying to look a jigsaw with lots of shades to blue and red and trying to see which ones were more important, it is possible, but not very reliable. From a general view, localisation from SPM5, with the ease of overlaying onto template brains and the use of coordinates that are transferable into the MRICRO program, which allows for even more scrutiny regarding localisation, seems to provide more confidence in the process of localising, and somewhat helps to narrow down the areas of interest, but then again, the GLM approach has the benefit of the model. FSL appears to show you much more and makes the job of discerning important and relevant localisations, not only more time consuming, but less reliable, as there is no direct method of overlaying the activations from independent components onto a stereotaxically homologous space. This vital tool, of being able to directly transfer to image acquisitions onto a definite brain, with landmark features to assist in the localisation, which by any stretch of the


imagination is not without the risk of human error, is a crucial failing of the FSL program approach to univariate analyses.

GLM attempts to extract a specific signal, that best fits the specified temporal model, identifying regions of the brain that activate with similar time courses to the experiment, depending on the Gaussian nature of the data. ICA extracts non-gaussian data from a Gaussian, mixed source rather than the data that best fit the model. ICA assumes that signal components are not only uncorrelated but also â&#x20AC;&#x2DC;statistically independentâ&#x20AC;&#x2122;, and attempts to find them from the mixed data-set of fMRI timecourses and assumes that these signals derive from physically different processes, thus maximising a measure of the joint entropy of the extracted signals (Friston, 1998). Both ICA and GLM can be used; if there is sound reason for specific hypothesis-testing, model-based approaches would be preferable, but if a model-based approach wouldnâ&#x20AC;&#x2122;t extract all the signals of interest (as has been demonstrated in this study), exploratory, data-driven methods can be more appropriate.

FSL exceeds SPM5 in terms of options for viewing results, with one-click changes from tables to graphs, displaying Gamma distributions of time courses that represent independent components, allowing you to assess their affinity to the experimental timings, and thus making sounder inferences regarding the relevance of a particular component (if the time course matches that of the experiment, it is likely to have something to do with experiment). You can plot graphs with SPM5, but it lags in the immediacy of the FSL program, and somewhat in its usefulness. SPM5 allows for whole


brain p-values, which create tables of MNI co-ordinates allowing for more certainty in localisation (with the use of MRICRO), whereas FSL cannot do this. As a first time user, these comments may merely show a lack of complete knowledge about the systems being discussed, but overall, there were mixed pros and cons with both SPM5 and FSL. During different stages of the data management and processing, each technique offered sometimes easier and sometimes more difficult sets of command chains to go through in order to do the necessary computations. Although the reanalysis did align generally to the results from the previous study, there were some discrepancies, and it would seem almost impossible for there not to be differences, because each time you reanalysed the same set of data, there are clearly far to many areas for operational error.

In summary, GLM allows for partitioning of variance, in understanding the degree to which each regressor explains the data. SPM5 computes a GLM on each voxel serially, demanding Gaussian distribution of data. ICA takes an essentially Gaussian mixture and tries to delineate the non-Gaussian distributions within it. It is suggested that the use of both methods concurrently, is so far the best option for trying to gather a holistic picture of brain activity during neuroimaging tasks, and especially in the current climate of comparing each method to establish its relative contribution; at this stage, neither stands out as a clear victor, so fortunately for neuroscientists, applying multiple statistical techniques of brain imaging data, seems to be the most sensible way forward


Understanding the neural mechanisms underlying active maintenance of task relevant information hinges on how we resolve the nature of stored representations, in addition to the types of operations performed on such representations (Dâ&#x20AC;&#x2122;Esposito, 2007). Representations equate to symbols and codes for information that are activated transiently or continuously within neural networks, and operations or processes (computations) are performed on these representations. Areas of the multimodal cortex (PFC & parietal cortex) are in a position to integrate representations through connectivity to the unimodal association cortex and are also critically involved in the active maintenance of task relevant information (Dâ&#x20AC;&#x2122;esposito, 2007). Fuster, (1997), expounds that the PFC is critically responsible for temporal integration and mediation of events that are separated in time such as in the ADD condition of this PASAT test. To explore this problem further specific results from both GLM and ICA will be discussed in relation to the original Hayter et al., (2007) findings.

Frontal Lobe

At 2nd level (between groups), GLM analysis found similarities with the original findings in the superior/middle frontal gyrus, cingulate gyrus, central sulcus, precentral gyrus and superior precentral sulcus.

ICA finds inferior frontal gyrus, superior frontal sulcus, medial orbital gyrus, inferior precentral sulcus, central gyrus, inferior precentral sulcus, superior postcentral sulcus, paracentral sulcus, superior lingual gyrus, which did not show up from the GLM.


According to Abdullaev (1997), bilateral premotor region (BA 6) â&#x20AC;&#x201C; middle frontal gyrus, activates due to stimulus effects, whereas BA 44/45 â&#x20AC;&#x201C; inferior frontal gyrus, shows effects of context but not stimulus and episode effects occur in both. The frontal lobe activity found here, seems to support rostro-caudal accounts of lateral PFC and premotor regions, dealing with cumulative cognitive demands, in a cascading manner and fits within the classical theory of executive control (Koechlin et al., 1997)

Ist level (individual differences) within GLM and ICA analyses highlight some other discrepancies between the results. Some examples from individuals upon whose results, both ICA and GLM analyses were computed.

Subject AM:

No common activations between GLM and ICA were found however GLM found almost equal number of activations that ICA did not, including the central sulcus and superior frontal sulcus. ICA found activity in the paracentral sulcus and superior postcentral sulcus. These differences could be down to minute discrepancies in localisation procedures, or they could reflect operational disparities between the 2 methods under discussion. Over all, these differing frontal activations, on their own, do not negate other findings, indeed they may add to them, if we take the view that both methods show us things that the other is not capable of, and by combining data, our understanding is further enriched. Paracentral, superior postcentral and central sulci


move dorsoventrally from medial cortical areas towards and into the superior frontal gyrus, it could be that these areas, play a significant role in the particular demands of the VWM task but it would be difficult establish this from group statistics alone, only by combining individual activations within 1st level GLM results and the robust components from the ICA, can we begin to see, how both methods, compliment, as oppose to exclude the other.

Parietal

2nd level GLM statistics, failed to retrieve any activations in the parietal lobe, despite their being found with ICA and previously in Hayter et al., (2007). Considering, that the GLM was input into the ICA, and the ICA managed to find similar activation in the IPS and various parts of the parietal lobe. It is an anomaly to this researcher as to why these results differed so extensively. On the basis that Hayter et al., (2007), found similar activations and several significant independent components were found in the parietal lobe during the ICA, it is proposed that these lacking data from GLM reflect some mistake that may have been made in the preprocessing stages, for there is no other obvious route to the discrepancy, and as such it is used an example of how, the resource intensive manual data entry, involved in the setting up of the model within the SPM5 programme, can allow for fairly huge differences in results, due to human error, that is extremely difficult to trace and remedy.

Based on the research, these parietal areas probably represent how the Parietal lobe is


critically involved in VWM. Majerus et al., (2006), found that the IPS is functionally connected to serial/temporal processing areas in the premotor and cerebellar cortices, as well as to phonological and orthographic processing areas in the superior temporal and fusiform gyri. This connectivity, suggest that parietal areas (the IPS especially) act as attentional modulators of distant neural networks that are known to deal with language and order information. The IPS, does seem to be influenced by cognitive load and demonstrates a fronto-parietal-cerebellar network for verbal short-term memory tasks, that is supported by our data. Majerus et al., (2006), also note the recent discovery of increased connectivity between IPS and medial superior frontal gyrus, cuneus and bilateral posterior cingulate cortex involved in visual mental imagery, so these activations seem to show that the Parietal areas of the brain, take part in managing and communicating information from higher order processing areas in the frontal areas, to multimodal cortex and towards the cerebellum, where automaticity of learning relieves the demands on WM structures in the prefrontal areas to deal with novel information.

Phonological processing is known to activate the left interior parietal lobe, posterior inferior frontal gyrus (Brocaâ&#x20AC;&#x2122;s area), premotor cortex and the cerebellum (Dâ&#x20AC;&#x2122;Esposito, 2007). It is interesting to note that all parietal activations discovered through ICA, were bilateral activations, which seem to suggest that their joint activity, is not only task-related, but almost certainly not noise. The chances that ICA would find perfectly bilateral activations in brain areas known to be implemented during VWM tasks, consistently across subjects, is far more indicative of the benefits of using ICA than the


possibility that these may be uncorrelated.

Temporal

At 2nd Level, GLM finds the insular lobe (as did Hayter et al., 2007), along with anterior and intermediate temporal gyri. ICA, on the other hand, does not find these areas, but implicates inferior-, superior- and middle-temporal gyri, along with the superior temporal sulcus, fusiform gyrus, subcollosal gyrus and parahippocampal gyrus. 1st level GLM statistics for subject SD showed superior temporal gyral activity that was not common to the ICA data, whereas subject FK, showed a common activation there for both GLM and ICA. AM, RO and SD, all display significant temporal lobe activations from the ICA, providing as yet, the most commonly activated area in this comparison. Temporal activity here, reflects the phonological component of the verbal task at hand. Majerus et al., (1996), state that the posterior superior temporal gyrus is involved in the processing of novel phonological information.

Subcortical

Caudate nucleus, subthalamic nucleus (putatively substantia nigra), and cerebellum, all displayed clear activity from the 2nd level GLM statistics, whereas ICA delineated the hippocampus, basal nucleus of the amygdala and crus cerebri, as being significantly activated during the experiment. At 1st level GLM analysis, lobule VII and crus II in the cerebellum were found in subjects AM and RO, caudate activations were discovered in


the results for subject AM and SD, and subject FK demonstrated activity in crus I of the cerebellum. Overwhelmingly, ICA could not pick up any of these findings, except for caudate activity, adding something by finding hippocampal activity in subject RO. Clearly, there is subcortical involvement in the demands of WM, but it seems as though GLM does a better job of retrieving these activations.

It is well established that the caudate nucleus, which is part of the striatum, is an integral part of the fronto-striatal circuit subserving cognitive functions (Abdullaev et al., 1997) with afferent connections from frontal and temporal-parietal cortical areas and efferents to the basal ganglia, it would seem that these networks contribute to the dorsolateral prefrontal and anterior cingulate pathways of cognition. One suggestion is that the head of the caudate is implicated in the prefrontal circuitry responsible for semantic and phonological computations in cognitive tasks (Abdullaev et al., 1997) providing a reasonable explanation for why caudate activity was found in this analysis, based on the phonological and higher order cognitive demands of the original task.

CONCLUSION:

Over all ICA finds more, but there are instances where ICA misses something that GLM finds, so using both methods is the surest way to get the fullest picture of what is going on in the brain during this task. One caveat to the ICA is that group analysis within stereotaxic space as opposed to individual brains is not possible at this stage. This


means that we can only make inferences about individual subjects and not between subjects, at least not statistically. GLM adheres to a rigid hypothesis, which is argued, may consider some significant activity as noise, which is why a combination of hypothesis driven and data-driven methods is forwarded.

There were many complicated steps in the analysis of this data, and SPM5 requires considerably more manual input, allowing for more potential human error. More checks would increase reliability in the statistics, and although, necessary reviews were made, more would be recommended to improve statistical confidence on the part of the researcher. Had there been more time, more space on the server and more brain power in the case of this novice attempt to understanding results from neuroimaging studies, stronger arguments could be made. In future, working with fresh data would be recommended and variations in the size of the smoothing kernel applied might highlight some interesting results. It is believed that the preprocessing and statistical analyses computed for this study, were the same as the original study, though it seems more likely that discrepancies in results between the 2 identical GLM analyses are either due to the use of a different SPM5 (instead of SPM2) programme, or that this first try, inevitably lead to some mistakes being made. The group was also limited in the storage space available on the serverâ&#x20AC;&#x2122;s hard-drive to compute additional analyses, not that there would have really been time or scope for this undergraduate thesis to deal with so much more detail.

A reanalysis of the Hayter et al., (2007) GLM analysis, revealed some consistent and


some discrepant findings of neuronal activity in areas that are now well established as being recruited during the multimodal and multi-sensory activity of performing a PASAT. Some suggestions for these discrepancies and evidence to support the consistencies have been made, along with more detail found from running an ICA on the same data. Overall, both statistical approaches, together, help to support the cascading model of hierarchical executive function (Koechlin et al., 2007). The relatively consistent prefrontal, parietal, temporal and subcortical activity found in this study, that to a greater extent contributes, as opposed to disputes the earlier findings in Hayter et al., (2007), seems to suggest a postero-anterior axis for corticosubcortical WM operations. This study supports the notion of a frontoparietal/temporal-cerebellar network for VSTM, which is evidenced by the work of Petrides (2005), who found that frontal area 47/12 had strong links to rostral inferotemporal visual association cortex and ventral limbic areas including rostral parts of the parrahippocampal gyrus. It is argued that active judegments on stimuli, typical to the requirements of the PASAT, are coded along a frontal mid-ventrolateral executive system that implements posterior association cortex during VWM tasks.

GLM results or ICA results alone, would not provide enough detail for one to make this conclusion, so the use of both techniques, in this case seems to have provided the clearest picture of what happens in the brain during this PASAT. Some of the strongest findings however, came from the ICA which brought attention to the Parietal lobe in particular, which is why this paper took the position it did, regarding fronto-caudal pathways which implicate a lot of the areas found in this investigation that may not


have been so well understood without the knowledge about the IPS which certainly represents the most robust finding from this study and the unique contribution that ICA has made to this line of research.

REFERENCES


Abdullaev, Y, G. & Melnichuk, K, V. (1997). Cognitive operations in the human caudate nucleus. Neuroscience Letters, 234, 151-155 Baddeley, A. (1986). Working Memory, Oxford: University press Blake, R. & Sekuler, R. (2006). Perception, London: McGraw Hill Chen, A. S. H. & Desmond, J. E. (2005). Temporal dynamics of cerebrocerebellar network recruitment during a cognitive task. Neuropsyhcologia, 43, 1227-1237 Curtis, C. E. & Dâ&#x20AC;&#x2122;Esposito, M. (2003) Persistent activity in the prefrontal cortex during working memory. TRENDS in Cognitive Science, 7, 415-424 Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., dale, A. M., Maguire, R. P., Hyman, B. T, Albert, M. S. & Killiany, R. J. (2006). An automated labelling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31, 968-980. Dâ&#x20AC;&#x2122;Esposito, M. (2007). From cognitive to neural models of workinh memory. Philosophical Transactions, 362, 761-772 Duvernoy, H. M. & Bourgouin, P. (1999). The Human Brain; Surface, ThreeDimensional Sectional Anatomy with MRI, and Blood Supply. Springer Friston, K. J. (1998). Modes or models: a critique on independent component analysis for fMRI. TRENDS in Cognitive Sciences, 2, 1227-1230 Fuster, J. M. (2004). Upper processing stages of the perception-action cycle. TRENDS in Cognitive Sciences, 8. 1-9 Hayter, A. L., Langdon, D. W. & Ramnani, N. (2007). Cerebellar contributions to working memory. Neuroimage, 36, 943-954 Hoover, J. E. & Strick, P. L. (1999). The organisation of Cerebellar and Basal Ganglia Outputs to the Primary Motor Cortex as revealed by retrograde Transneuronal Transport of Herpes Simplex Virus Type 1. Journal of Neuroscience,15, 1446-1463 Horwitz, B., Tagamets, M. A. & McIntosh, A. R. (1999). Neural modelling of functional brain imaging, and cognition. Trends in Cognitive Sciences, 3, 91-98 Kelly, R. M. & Strick, P. L. (2003). Cerebellar Loops with Motor Cortex and Prefrontal cortex of a nonhuman primate. The Journal of Neuroscience, 23, 8432-8444


Koechlin, E. & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. TRENDS in Cognitive Science, 11, 229-236 Koechlin, E., Ody, C. & Kouneiher, F. (2007). The Architecture of Cognitive Control in the Human Prefrontal Cortex. Science, 302, 1181-1187 Jung, T., Makeig, S., Lee, T., McKeown, M. J., Brown, G., Bell, A. J. & Sejnowski, T. J. (2000). Independent component analysis of biomedical signals. Proceedings of the 2nd International workshop on ICA and Blind source separations, 633-644. Majerus, S., Poncelet, M., Linder, Van der., Albouy, G., Salmon, E., Sterpenich, V., Vandewalle, G., Collete, F. & Maquet, P. (2006). The left interparietal sulcus and verbal short-term memory: Focus of attention or serial order? Neuroimage, 32, 880-891 McKeown, M. J., Jung, T, Makeig, S., Brown, G, Kindermann, S. S., Lee, T. & Sejnowski, T. J (1997). Spatially independent activity patterns in functional MRI data during the Stroop color naming task. National Academy of Sciences, 95, 803-810 Middleton, F. A. & Strick, P. L. (2001). Cerebellar projections to the prefrontal cortex of the primate. Journal of Neuroscience, 21, 700-712 Pandya, D. N., Yeterian, E. H., Fleminger, S. & Dunnett S. B (1996). Comparison of prefrontal architecture and connections. Philosophical Transactions: Biological Sciences, 351, 1423-1432 Passingham, D. & Sakai, K. (2004). The prefrontal cortex and working memory; physiology and brain imaging. Current Opinions in Neurobiology, 14, 163-168 Petrides, M. (1986). The effect of periarcuate lesions in the monkey on the performance of symmetrically and asymmetrically reinforced visual and auditory go, no- go tasks. Journal of Neuroscience, 6, 2054-2063 Petrides, M. & Pandya, D. N. (2001) Dorsolateral PFC: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. European journal of Neuroscience, 11, 10111036 Petrides, M. & Pandya, D. N. (2001). Comparative cytoarchitecture analysis of the human and macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey. European Journal of Neuroscience, 16, 291310


Petrides, M. (2005). Lateral prefrontal cortex: architectonic and functional organisation.  Philosophical Transactions of the Royal Society, 360, 781-795 Ramnani, N. (2006). The primate cortico-cerebellar system; anatomy and function. Nature Reviews: Neuroscience, 7, 511-522 Ramnani, N., Behrens, T. E. J., Johansen-Berg, H., Richter, M. C., Pinsk, M. A., Andersson, J. L. R., Rudebeck, P., Ciccarelli, O., Richter, W., Thompson, A. J., Matthews, D. R., Kastner, S. & Matthews, P. M. (2006). The evolution of prefrontal inputs to the cortico-pontine sstem: Diffusion imaging evidence from macaque monkeys and Humans. Cerebral Cortex, 16, 811-818 Silveri, M. C., Di Betta, A. M., Filippini, V., Leggio, M. G. & Molinari, M. (1998). Verbal short-term store rehearsal system and the cerebellum: evidence from a patient with a right cerebellar lesion. Brain, 121, 2175-2187 Smith, E. E. & Jonides, J. (1996). Neuroimaging analyses of human working memory. National Academy of Science, 48109-48110 Tombaugh, T, N. (2006). A comprehensive review of the Paced Auditory Serial Addition Test (PASAT). Archives of Clinical Neuropsychology, 21, 53-76 Walker, A. E. (1940). A cytoarchitectural study of the prefrontal area of the macaque monkey. Journal of Computational Neurology, 73, 59-86 Wolpert, D. M., Miall, C. & Kawato, M. (1998). Internal models in the cerebellum. TRENDS in Cognitive Sciences, 2, 338-348 Yates, S. L., Barach, A., Gingell, S., Whalley, H. C., Job, D., Johnstone, E. C, Best, J. J. K. & Lawrie, S. M. Parcellating the temporal lobes from magnetic resonance images using generic software in subjects at high risk of developing schizophrenia. (2006) Psychiatry Research: Neuroimaging. 147, 197-212


Model free approaches to investigating the neural mechanisms of working memory