The Effects of Meditation on the Dorsal Nexus and Neural Networks associated with MDD

Page 1

The Effects of Meditation on the Dorsal Nexus and Neural Networks Associated with Major Depressive Disorder

NEW331S – Science of Mindfulness Meditation

Zachary Johnson 996629340 Pr. John Vervaeke Friday, April 12th, 2013

2 As the most common mental disorder, depression affects more than 350 million people globally (WHO, 2012). For the treatment of major depressive disorder (MDD), pharmacological treatments (Howland, 2006; Weinmann, Becker, & Koesters, 2008), repetitive transcranial stimulation (Dell’Osso et al., 2011; Eschweiler et al., 2000), cognitive behavioural therapy (Butler, Chapman, Forman, & Beck, 2006; Lynch, Laws, & McKenna, 2010), interpersonal therapy (Cuijpers et al., 2011; Kotova, 2005), and exercise (Rimer et al., 2011) have proven effective. Neurochemical (Saldanha, Kumar, Ryali, Srivastava, & Pawar, 2009), subgenual cingulate cortex (Gotlib et al., 2005), and default network (Berman et al, 2011) abnormalities have been used to explain MDD’s neural basis. As a promising new neuroscientific account of the disorder, the dorsal nexus theory (DNT) suggests that in MDD, the dorsomedial prefrontal cortex (dorsal nexus [DN]) activity and connectivity to various brain networks is abnormal, resulting in the effective “hotwiring” of these networks together (Sheline, Price, Yan, & Mintun, 2010). In addition to direct research, this theory uses specific findings, such as those described above, to describe MDD’s mechanisms from a more integrationist and “whole brain” perspective. It also seems to recognize a variety of neural circuit irregularities associated with a wider range of MDD symptoms. Given the importance of this theory, this paper will aim to demonstrate how MDD abnormalities in the affective, cognitive control, default mode, and executive networks are related to the DNT and how each of these abnormalities corresponds to faulty heuristics explained by a parasitic processing model of psychopathology. Further, evidence will be presented that examines the importance of mindfulness meditation (MM) in regulating these

3 abnormalities through its effects on these networks during practice. Neuroscientific MDD evidence pertaining to the DNT and related neural networks will be presented, followed by neuropsychobehavioural evidence related to the faulty heuristics of the parasitic processing model, neuroscientific evidence pertaining to the effects of MM on these networks, and finally, an integration that will connect the neuroscientific and neuropsychobehavioural evidence. To investigate the role of the DN in MDD, Sheline, Price, Yan and Mintun (2010), examined the connectivity patterns between three different networks—the cognitive control network, the default mode network, and the affective network—in relation to the DN. Resting-state fMRI scanning was conducted to measure, compared to controls, levels of connectivity to the DN for MDD participants. When comparing the two groups, an ANCOVA statistical analysis indicated that MDD participants showed significantly more connectivity between all three networks and the dorsal nexus compared to controls (p < 0.0001). Age (p = 0.70), sex (p = 0.97), and education (p = 0.47) did not have a significant effect. In addition, DN connectivity values were positively correlated with Hamilton Depression Rating Scale (HAMD) scores (p < 0.0001). The authors argue that these results indicate that the DN provides a mechanism to better explain how distinct neural networks contribute to depressive symptomatology through the synergistic and concurrent nature of cognitive focus, self-focus, increased vigilance, and emotional, visceral, and autonomic regulation. They also suggest that these results demonstrate the possible therapeutic value of reducing DN connectivity.

4 The next four studies looked at patterns of activation and connectivity in various networks common in MDD. Zhu et al (2012) examined default network activity in resting-state treatment-na誰誰ve MDD patients. Related psychopathological characteristics such as depressive rumination and over-general autobiographical memory were investigated. fMRI scanning was conducted during resting states on young, treatment-na誰誰ve MDD participants (n = 35) matched with healthy controls (n = 35). Independent component analyses indicated that MDD participants displayed increased functional connectivity in the MPFC (p < 0.05) and ACC (p < 0.05), while displaying decreased functional connectivity in the PCC (p < 0.05) and the precuneus (p < 0.05) when compared to controls. MDD subjects also displayed higher levels of rumination and over-general autobiographical memory than did controls (p < 0.001). In the MDD group, correlations were found between increased MPFC/ACC connectivity and depressive rumination score (p < 0.01), as well as between decreased PCC/precuneus connectivity and over-general autobiographical memory (p < 0.05). The authors reported a dissociation between anterior (MPFC/ACC) and posterior (PCC/precuneus) functional connectivity in MDD patients and suggest that this evidence demonstrates the probability of the relation between MDD traits and psychopathology and abnormalities in the DMN. Although Alexopoulos et. al (2012) similarly investigated abnormal DMN activity patterns associated with MDD, they also examined CCN (cognitive control network) functional connectivity in late-life participants with the disorder. MDD participants (n = 16) who were non-demented and non-MCI (mild cognitive impairment) were compared with controls (n = 10) using fMRI during resting states.

5 Assessments of depressive symptoms (MADRS), cognitive impairment (MMSE), dementia (DRS), response inhibition (Stroop color word test), visual attention and task switching (Trails A/B), dysexecutive behaviour (FSBS), apathy (AES), and memory (HVLT-R) were conducted. Functional connectivity of the CCN was determined by placing seeds in the dACC and the DLPFC bilaterally, and by placing a seed in the PCC to assess DMN connectivity. The results indicated that MDD participants demonstrated significant decreases in functional connectivity in the CCN (p < 0.05) and significant increases in functional connectivity in the DMN (p < 0.05). In the CCN, MDD participants demonstrated decreased connectivity of the DLPFC and the bilateral inferior parietal cortices (p < 0.05). In the DMN, MDD participants demonstrated increased connectivity in the precuneus, subgenual ACC, VMPFC, and lateral parietal regions (p < 0.05). Low CCN functional connectivity predicted low remission rates and persistence of depressive symptoms (p < 0.001), and high functional connectivity of the DMN predicted pessimism (p = 0.034). The authors argue that both networks are involved in late-life MDD and that the results imply the importance of these faulty connectivity patterns in the disorder. MDD can also be understood using an affective emotional model. Zhong et. al. (2011) addressed the question of amygdala and DLPFC activity in participants who had medication-free MDD (n = 26), healthy subjects with cognitive vulnerabilities to depression (n = 26), and demographically matched healthy controls (n = 31). Participants were scanned using fMRI while performing an emotional matching task. Those with MDD showed increased activation in the amygdala and insula and reduced DLPFC activation relative to healthy controls (p < 0.05). The same activation

6 pattern was noticed in subjects with cognitive vulnerabilities to depression when compared to controls (p < 0.05). The authors posit that the findings demonstrate that MDD and cognitive vulnerabilities to depression are characterized by hypoactivation of the prefrontal cortex and hyperactivation of the amygdala in response to emotional stimuli. They also suggest the importance of these two areas in the regulation of neural networks related to MDD, and more specifically, the possibility of the failure to regulate the amygdala via the DLPFC in reaction to emotional stimuli. Research has also shed light on cognition—often considered an important component of mood regulation (Graupmann, 2008)—in MDD. Desseilles et al. (2009) attempted to clarify how the regions involved in affective processing interact with attentional correlates. To do this, they tested whether MDD altered attentional effects on neural filtering when participants were presented with irrelevant, nonemotional stimuli. fMRI scanning was conducted on non-medicated, first-episode MDD subjects (n = 14) compared to healthy matched controls (n = 14). Participants engaged in easy and difficult tasks (based on attentional load at fixation) while irrelevant colored stimuli were presented in the periphery. The behavioural data revealed that MDD subjects missed more targets than did controls in the neural filtering task, and that they also made more errors in the high-load condition (p = 0.031). The fMRI results indicated that both groups engaged VLPFC and superior parietal regions during the high-load task (p < 0.001), but in the low-load condition, MDD subjects displayed decreases in activation in the calcarine sulcus (primary visual cortex)—an area that is known to be modulated by central load

7 manipulation (Schwartz et al., 2005) and corresponds to the V4 visual area responsible for colour responsiveness (Bartels & Zeki, 2000). Functional connectivity analysis revealed that there was increased connectivity between the IPS and V4 and the right VLPFC in the low-load condition for controls, but not for MDD subjects (p < 0.001). The authors argue that these results show an abnormal filtering of irrelevant stimuli, along with dysfunctional functional connectivity between frontoparietal networks and visual cortices, in MDD subjects. They also suggest that in MDD, important cognitive resources are engaged even in a simple pop-out task, thereby restricting the processing of peripheral distractors and possibly reducing the spatial spanning of attention. To connect the neuroscientific evidence regarding MDD’s effects on the brain with the evidence (to be reviewed later) regarding MM’s effects on the brain, Vervaeke and Ferraro’s (2013) neuropsychobehavioural account of pathology proves helpful. These authors argue that the very dynamic brain systems that are responsible for intelligence are also responsible for foolishness. In this model, the brain’s dynamic processes can lead to self-destructive feedback loops that become stable, and thus difficult to change, because they take over the brain’s self-organizing criticality. This phenomenon has been termed “parasitic processing.” In this model, cognitive biases and faulty heuristics interact and reinforce each other such that a newly shaped system emerges within one’s cognition that is highly resilient and compulsive (due to positive feedback). Among the faulty biases and heuristics involved, four are relevant to this discussion. The first is the availability heuristic. In this process, individuals engage in a mental shortcut that assesses an event’s

8 probability by how easy it is to think of relevant examples. This heuristic runs on the notion “if you think of it, it must be important.” For example, suppose one were asked “Are there more males or females at your school?” If one responds with an answer dependent on remembered instances of encountering that gender, one would be falling prey to this heuristic. The next error in thinking is the representative bias. This, too, is a probabilistic error of uncertainty. It is defined as “the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated” (Kahneman & Tversky, 1972, 1973). Those prone to this error mistake how representative something is with how likely it is. Next is the affective heuristic. In this error, individuals reduce the quantity of information they must search through by using emotional cues to solve problems—an approach synonymous with “going with your gut feeling.” Feelings of anger, fear, sadness, surprise, happiness, and disgust, among others, can be used to rapidly evaluate various situations. Finally, there is the confirmation bias. Here, people tend to acknowledge only information that confirms or agrees with their beliefs and understandings. People who experience this bias will often selectively ignore counter-evidence that would disprove deeply held beliefs. Once again, in the development of psychopathologies, all of these biases should be understood to interact with each other dynamically in taking over the brain’s self-organizing criticality. Research will now be presented pertaining to MM’s effects on various neural networks. Hasenkamp and Barsalou (2012) aimed to investigate the effect of MM experience on brain networks responsible for the cognitive mechanisms active

9 during MM. In a previous study (Hasenkamp, Wilson-Mendenhall, Duncan, & Barsalou, 2012), the authors created a cognitive action model that mapped the mental states that occur in focused attention meditation—specifically, mind wandering, awareness of mind wandering, shifting of attention, and sustained attention. The salience network was associated with awareness of mind wandering, the executive network with shifting and sustaining attention, and the default network with mind wandering. Subsequently, Hasenkamp and Barsalou (2012) aimed to determine whether years of meditative practice created long-lasting functional connectivity changes in the networks associated with the mental states just listed. To test this hypothesis, seeds were placed in the areas associated with each neural network, and fMRI scanning revealed resting-state connectivity patterns. These patterns were compared between participants with either high or low meditation experience. Participants with more meditation experience demonstrated increased connectivity within attentional networks (p < 0.005), in addition to greater connectivity between attentional regions and medial prefrontal regions (p < 0.005). The authors argue that these neural patterns may be involved in protection against distraction via increased attentional engagement. They also suggest that these findings may represent a transfer of mental abilities from practice to everyday life in those with experience. Focusing on the executive functioning of attention in meditation, Manna et al. (2010) looked at the attentional regulatory practice of two styles of meditation: “focused attention” and “open monitoring.” Theravadan Buddhist monks (n = 8) who were experts in both styles were compared to novice meditators (n = 8) who had

10 received a ten-day course in the two styles. fMRI scanning was conducted on both groups as participants engaged in the two styles of meditation. Statistical analyses revealed that the DLPFC was activated in both styles. In the “open monitoring” style, DLPFC activity was associated with activity in the superior parietal lobule for expert meditators, in addition to increased activation in the MPFC (p < 0.01). As well, the relationship between the DLPFC and the ACC in one style was the opposite of that in the other. In the “focused attention” style, DLPFC activity was associated with ACC activity (p < 0.01). In the “open monitoring” style, DLPFC activity was associated with ACC deactivation (p < 0.01). Based on this evidence, the authors argue that expert meditators engage cognitive control of sensory-related thought and emotion through an enormous self-regulation of fronto-parietal areas related to attention. MM has also been shown to have an impact on the affective mechanisms related to emotion. Farb et al. (2010) sought to investigate the alteration of the neural expression of sadness produced by mindfulness practice. Participants (n = 36) were randomly assigned to either a MBSR (Mindfulness-Based Stress Reduction) group (n = 20) or a control waitlisted group (n = 16). Assessments for Depression (BDI-II), anxiety (BAI), and psychopathological symptoms (SCL-90-R) were conducted. In the procedure, participants alternated between viewing neutral and sad video clips, with rest delays between the clips. Despite equivalent selfreported ratings of sadness, the fMRI scanning that was conducted while participants viewed the clips revealed that those with mindfulness training displayed greater lateralized activation in the insula, VLPFC, and the superior frontal gyrus (p < 0.001)—all associated with visceral and somatosensory body sensation.

11 Conversely, controls displayed larger activations in the cortical midline—in regions such as the VMPFC, DMPFC, PCC, and the precuneus (p < 0.001) —associated with ruminative and self-reflective processing. In addition, the somatic activations noticed in those who received meditation training were associated with decreased depression scores (p < 0.001). The authors suggest that these results demonstrate how important the midline cortical structures are in those undergoing dysphoric mood provocation. Further, they point toward the importance of mindfulness in recruiting more sensory and visceral neural structures and how this effect can lead to reductions in affective reactivity and disorder vulnerability. Other research has considered the roles of attention and cognition in MM. For example, Allen et al. (2012) aimed to disentangle the respective roles of interoceptive salience and attentional control associated with the self-regulatory effect of mindfulness training. To address this issue, fMRI was used to measure behavioural meta-cognition against whole-brain BOLD (blood oxygenation leveldependent) signals during an affective Stroop task before (n = 31) and after mindfulness training (n = 30) interventions in healthy participants. After treatment and cognitive effects were controlled for, subjects who had received mindfulness training showed reduced affective Stroop conflict (p = 0.032) and greater DLPFC activation (p = 0.03) during executive processing, suggesting the enhancement of cognitive control. In addition, participants who received the greatest amount of mindfulness training practice displayed improvements in response inhibition through the recruitments of the dACC, anterior insula, and MPFC during negative

12 valence processing. The authors argue that these results demonstrate the importance of active cognitive control associated with mindfulness training. Finally, MM has been shown to affect the default network. Brewer et al. (2011) assessed brain activity in experienced meditators (n = 12) and matched meditation-naïïve controls (n = 13) as they performed three different types of meditation: concentration, loving-kindness, and choiceless awareness. fMRI scanning revealed that the main areas associated with the default network— specifically, the MPFC and PCC—were relatively deactivated in experienced mediators across all forms of meditation (p < 0.05). Connectivity analyses demonstrated that there were stronger connections between the PCC, dACC, and DLPFC (p < 0.05). Based on this evidence, the authors argue that the neural correlates and connectivity patterns display the differences in the default network consistent with decreased mind-wandering. They also suggest that the evidence entails many clinical implications due to the association between the default network and many forms of psychopathology. Given the evidence provided, this paper’s thesis—that MDD abnormalities in the affective, cognitive control, default mode, and executive networks are related to the DNT, and correspond to a parasitic processing model of psychopathology, and further, that MM regulates these abnormalities through its effects on these networks —is well supported. The neuroscientific evidence pertaining to MDD and the DNT reveals two interesting patterns. The first is the increased connectivity of the DN (MPFC) in the cognitive control, default mode, and affective networks (Sheline, Price, Yan, & Mintun, 2010; Zhu et al., 2012). The second is the decreased lateral

13 recruitment and connectivity in the DLPFC and the VLPFC for the executive, affective, and cognitive control networks (Alexopoulos et al., 2012; Desseilles et al., 2009; Zhong et al., 2011). These results reveal that over-connectivity to the DN and under-recruitment and connectivity of lateral prefrontal regions are key neural abnormalities in MDD. Connecting the MDD evidence to the parasitic processing model, it becomes evident that parallels exist between default network abnormalities and the availability heuristic, between executive network abnormalities and the representative bias, between affective network abnormalities and the affective heuristic, and between cognitive control abnormalities and the confirmation bias. Specifically, increases in ruminative depression scores associated with MPFC/ACC activity can be linked to the availability of information that would be necessary to maintain spontaneous, self-reflective, random thoughts. With regard to executive function, the inability of people with MDD to adequately filter irrelevant information would play into the representative bias: MDD subjects might choose the more representative thoughts that emerge from the default network. The failure to regulate the amygdala via the DLPFC in the affective network would correspond to the increased emotional response to inadequately filtered thoughts seen with the affective heuristic. Finally, the decreased executive network activity of the DLPFC in MDD subjects parallels the inability to use rule-based, rational thinking to consider counter-evidence characteristic of the confirmation bias. In MM, the greater MDD activity and connectivity in the DN (MPFC) for affective and default networks (Sheline, Price, Yan, & Mintun, 2010; Zhu et al., 2012) was reversed, such that the DN (MPFC) was less connected and activated (Brewer et

14 al., 2011; Farb et al., 2010). In addition, areas that showed decreased MDD recruitment of DLPFC and VLPFC (Alexopoulos et al., 2012; Desseilles et al., 2009; Zhong et al., 2011) were increased in MM for the executive, affective, and default networks (Allen et al., 2012; Brewer et al., 2011; Manna et al., 2010; Farb et al., 2010). In addition, Hasenkamp and Barsalou (2012) reported greater connectivity between the executive network and the MPFC and suggested that this might help protect against distractive cognitions. Through the change in neural activity associated with MM, various network and DN abnormalities can be improved. To conclude, evidence for MM’s potential effectiveness on MDD through the regulation of the DN and various network abnormalities appears strong. Although a variety of other interventions have proven effective in MDD treatment, few use an integrative, “whole brain” perspective to understand the abundance of symptoms associated with MDD and how they influence each other. By connecting the MDD neuroscientific evidence to the parasitic processing model, it becomes clear how the very dynamical system that affords human intelligence also affords foolishness, and more importantly, a potential vulnerability to psychopathology. The MM evidence indicates that the destructive MDD architecture can be remodelled with practice to transform foolish and self-destructive patterns of behaviour into intelligent ones that promote positive growth.

Turn static files into dynamic content formats.

Create a flipbook
Issuu converts static files into: digital portfolios, online yearbooks, online catalogs, digital photo albums and more. Sign up and create your flipbook.