Dysregulated left inferior parietal activity in schizophrenia and depression: functional connectivity and characterization

Brain structural alterations associated with young women with subthreshold depression

Neuroanatomical abnormalities in patients with major depression disorder (MDD) have been attracted great research attention. However, the structural alterations associated with subthreshold depression (StD) remain unclear and, therefore, require further investigation. In this study, 42 young women with StD, and 30 matched non-depressed controls (NCs) were identified based on two-time Beck Depression Inventory scores. Whole-brain voxel-based morphometry (VBM) and region of interest method were used to investigate altered gray matter volume (GMV) and white matter volume (WMV) among a non-clinical sample of young women with StD. VBM results indicated that young women with StD showed significantly decreased GMV in the right inferior parietal lobule than NCs; increased GMV in the amygdala, posterior cingulate cortex, and precuneus; and increased WMV in the posterior cingulate cortex and precuneus. Together, structural alterations in specific brain regions, which are known to be involved in the fronto-limbic circuits implicated in depression may precede the occurrence of depressive episodes and influence the development of MDD.

Visuospatial and mathematical dysfunction in major depressive disorder and/or panic disorder: A study of parietal functioning

The parietal cortex is critical for several different cognitive functions, including visuospatial processing and mathematical abilities. There is strong evidence indicating parietal dysfunction in depression. However, it is less clear whether anxiety is associated with parietal dysfunction and whether comorbid depression and anxiety are associated with greater impairment. The present study compared participants with major depression (MDD), panic disorder (PD), comorbid MDD/PD and controls on neuropsychological measures of visuospatial processing, Judgement of Line Orientation (JLO), and mathematical abilities, Wide Range Achievement Test (WRAT) Arithmetic. Only comorbid MDD/PD was associated with decreased performance on JLO, whereas all psychopathological groups exhibited comparably decreased performance on WRAT Arithmetic. Furthermore, the results were not accounted for by other comorbid disorders, medication use or psychopathology severity. The present study suggests comorbid depression and anxious arousal are associated with impairment in visuospatial processing and provides novel evidence indicating mathematical deficits across depression and/or anxiety. Implications for understanding parietal dysfunction in internalising psychopathology are discussed.

Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups.

Meta-analytic connectivity modeling revisited: controlling for activation base rates

Co-activation of distinct brain regions is a measure of functional interaction, or connectivity, between those regions. The co-activation pattern of a given region can be investigated using seed-based activation likelihood estimation meta-analysis of functional neuroimaging data stored in databases such as BrainMap. This method reveals inter-regional functional connectivity by determining brain regions that are consistently co-activated with a given region of interest (the "seed") across a broad range of experiments. In current implementations of this meta-analytic connectivity modeling (MACM), significant spatial convergence (i.e. consistent co-activation) is distinguished from noise by comparing it against an unbiased null-distribution of random spatial associations between experiments according to which all gray-matter voxels have the same chance of convergence. As the a priori probability of finding activation in different voxels markedly differs across the brain, computing such a quasi-rectangular null-distribution renders the detection of significant convergence more likely in those voxels that are frequently activated. Here, we propose and test a modified MACM approach that takes this activation frequency bias into account. In this new specific co-activation likelihood estimation (SCALE) algorithm, a null-distribution is generated that reflects the base rate of reporting activation in any given voxel and thus equalizes the a priori chance of finding across-study convergence in each voxel of the brain. Using four exemplary seed regions (right visual area V4, left anterior insula, right intraparietal sulcus, and subgenual cingulum), our tests corroborated the enhanced specificity of the modified algorithm, indicating that SCALE may be especially useful for delineating distinct core networks of co-activation.

Interindividual differences in cognitive flexibility: influence of gray matter volume, functional connectivity and trait impulsivity

Cognitive flexibility, a core aspect of executive functioning, is required for the speeded shifting between different tasks and sets. Using an interindividual differences approach, we examined whether cognitive flexibility, as assessed by the Delis-Kaplan card-sorting test, is associated with gray matter volume (GMV) and functional connectivity (FC) of regions of a core network of multiple cognitive demands as well as with different facets of trait impulsivity. The core multiple-demand network was derived from three large-scale neuroimaging meta-analyses and only included regions that showed consistent associations with sustained attention, working memory as well as inhibitory control. We tested to what extent self-reported impulsivity as well as GMV and resting-state FC in this core network predicted cognitive flexibility independently and incrementally. Our analyses revealed that card-sorting performance correlated positively with GMV of the right anterior insula, FC between bilateral anterior insula and midcingulate cortex/supplementary motor area as well as the impulsivity dimension "Premeditation." Importantly, GMV, FC and impulsivity together accounted for more variance of card-sorting performance than every parameter alone. Our results therefore indicate that various factors contribute individually to cognitive flexibility, underlining the need to search across multiple modalities when aiming to unveil the mechanisms behind executive functioning.

Neural networks related to dysfunctional face processing in autism spectrum disorder

One of the most consistent neuropsychological findings in autism spectrum disorders (ASD) is a reduced interest in and impaired processing of human faces. We conducted an activation likelihood estimation meta-analysis on 14 functional imaging studies on neural correlates of face processing enrolling a total of 164 ASD patients. Subsequently, normative whole-brain functional connectivity maps for the identified regions of significant convergence were computed for the task-independent (resting-state) and task-dependent (co-activations) state in healthy subjects. Quantitative functional decoding was performed by reference to the BrainMap database. Finally, we examined the overlap of the delineated network with the results of a previous meta-analysis on structural abnormalities in ASD as well as with brain regions involved in human action observation/imitation. We found a single cluster in the left fusiform gyrus showing significantly reduced activation during face processing in ASD across all studies. Both task-dependent and task-independent analyses indicated significant functional connectivity of this region with the temporo-occipital and lateral occipital cortex, the inferior frontal and parietal cortices, the thalamus and the amygdala. Quantitative reverse inference then indicated an association of these regions mainly with face processing, affective processing, and language-related tasks. Moreover, we found that the cortex in the region of right area V5 displaying structural changes in ASD patients showed consistent connectivity with the region showing aberrant responses in the context of face processing. Finally, this network was also implicated in the human action observation/imitation network. In summary, our findings thus suggest a functionally and structurally disturbed network of occipital regions related primarily to face (but potentially also language) processing, which interact with inferior frontal as well as limbic regions and may be the core of aberrant face processing and reduced interest in faces in ASD.

Comparison of structural covariance with functional connectivity approaches exemplified by an investigation of the left anterior insula

The anterior insula is a multifunctional region involved in various cognitive, perceptual and socio-emotional processes. In particular, a portion of the left anterior insula is closely associated with working memory processes in healthy participants and shows gray matter reduction in schizophrenia. To unravel the functional networks related to this left anterior insula region, we here combined resting state connectivity, meta-analytic-connectivity modeling (MACM) and structural covariance (SC) in addition to functional characterization based on BrainMap meta-data. Apart from allowing new insight into the seed region, this approach moreover provided an opportunity to systematically compare these different connectivity approaches. The results showed that the left anterior insula has a broad response profile and is part of multiple functional networks including language, memory and socio-emotional networks. As all these domains are linked with several symptoms of schizophrenia, dysfunction of the left anterior insula might be a crucial component contributing to this disorder. Moreover, although converging connectivity across all three connectivity approaches for the left anterior insula were found, also striking differences were observed. RS and MACM as functional connectivity approaches specifically revealed functional networks linked with internal cognition and active perceptual/language processes, respectively. SC, in turn, showed a clear preference for highlighting regions involved in social cognition. These differential connectivity results thus indicate that the use of multiple forms of connectivity is advantageous when investigating functional networks as conceptual differences between these approaches might lead to systematic variation in the revealed functional networks.