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1. Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting‐State Functional MRI After Working Memory Training in People With HIV

   https://doi.org/10.1002/jmri.28439  

   Jia, Chunying ; Long, Qunfang ; Ernst, Thomas ; Shang, Yuanqi ; Chang, Linda ; Adali, Tülay ( September 2022 , Journal of Magnetic Resonance Imaging)

   Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.

   To test whether WMT affects PWH brain functional connectivity in resting‐state fMRI (rsfMRI).

   Prospective.

   A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53HIV‐seronegative controls (SN, ages 49.5 ± 1.6 years, six women).

   Axial single‐shot gradient‐echo echo‐planar imaging at 3.0 T was performed at baseline (TL1), at 1‐month (TL2), and at 6‐months (TL3), after WMT.

   All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training,n = 58: 28 PWH, 30 SN; nonadaptive training,n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.

   Two‐way analyses of variance (ANOVA) on GT metrics and two‐samplet‐tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) usingmore »Pearson's correlations. The significance level was set atP < 0.05 after false discovery rate correction.

   The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).

   ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.

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2. A neuropsychosocial signature predicts longitudinal symptom changes in women with irritable bowel syndrome

   https://doi.org/10.1038/s41380-021-01375-9  

   Bhatt, Ravi R. ; Gupta, Arpana ; Labus, Jennifer S. ; Liu, Cathy ; Vora, Priten P. ; Jean Stains ; Naliboff, Bruce D. ; Mayer, Emeran A. ( November 2021 , Molecular Psychiatry)

   Irritable bowel syndrome (IBS) is a common disorder of brain-gut interactions characterized by chronic abdominal pain, altered bowel movements, often accompanied by somatic and psychiatric comorbidities. We aimed to test the hypothesis that a baseline phenotype composed of multi-modal neuroimaging and clinical features predicts clinical improvement on the IBS Symptom Severity Scale (IBS-SSS) at 3 and 12 months without any targeted intervention. Female participants (N = 60) were identified as “improvers” (50-point decrease on IBS-SSS from baseline) or “non-improvers.” Data integration analysis using latent components (DIABLO) was applied to a training and test dataset to determine whether a limited number of sets of multiple correlated baseline’omics data types, including brain morphometry, anatomical connectivity, resting-state functional connectivity, and clinical features could accurately predict improver status. The derived predictive models predicted improvement status at 3-months and 12-months with 91% and 83% accuracy, respectively. Across both time points, non-improvers were classified as having greater correlated morphometry, anatomical connectivity and resting-state functional connectivity characteristics within salience and sensorimotor networks associated with greater pain unpleasantness, but lower default mode network integrity and connectivity. This suggests that non-improvers have a greater engagement of attentional systems to perseverate on painful visceral stimuli, predicting IBS exacerbation. The ability ofmore »baseline multimodal brain-clinical signatures to predict symptom trajectories may have implications in guiding integrative treatment in the age of precision medicine, such as treatments targeted at changing attentional systems such as mindfulness or cognitive behavioral therapy.

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3. An information network flow approach for measuring functional connectivity and predicting behavior

   https://doi.org/10.1002/brb3.1346  

   Kumar, Sreejan ; Yoo, Kwangsun ; Rosenberg, Monica D. ; Scheinost, Dustin ; Constable, R. Todd ; Zhang, Sheng ; Li, Chiang‐Shan R. ; Chun, Marvin M. ( July 2019 , Brain and Behavior)

   Connectome‐based predictive modeling (CPM) is a recently developed machine‐learning‐based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions’ fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy.

   With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) (n = 25), we use the CPM framework to build machine‐learning models that predict attention from FC patterns measured with information flow. Models trained onn − 1 participants’ task‐based patterns were applied to an unseen individual's resting‐state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting‐state fMRI data and a measure of attention (Attention Network Task performance [n = 41] and stop‐signal task performance [n = 72]).

   Our model significantly predicted individual differences in attention task performance across three different datasets.

   Information flow may be amore »useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.

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4. Brain network coupling associated with cognitive performance varies as a function of a child’s environment in the ABCD study

   https://doi.org/10.1038/s41467-021-27336-y  

   Ellwood-Lowe, Monica E. ; Whitfield-Gabrieli, Susan ; Bunge, Silvia A. ( December 2021 , Nature Communications)

   Prior research indicates that lower resting-state functional coupling between two brain networks, lateral frontoparietal network (LFPN) and default mode network (DMN), relates to cognitive test performance, for children and adults. However, most of the research that led to this conclusion has been conducted with non-representative samples of individuals from higher-income backgrounds, and so further studies including participants from a broader range of socioeconomic backgrounds are required. Here, in a pre-registered study, we analyzed resting-state fMRI from 6839 children ages 9–10 years from the ABCD dataset. For children from households defined as being above poverty (family of 4 with income > $25,000, or family of 5+ with income > $35,000), we replicated prior findings; that is, we found that better performance on cognitive tests correlated with weaker LFPN-DMN coupling. For children from households defined as being in poverty, the direction of association was reversed, on average: better performance was instead directionally related to stronger LFPN-DMN connectivity, though there was considerable variability. Among children in households below poverty, the direction of this association was predicted in part by features of their environments, such as school type and parent-reported neighborhood safety. These results highlight the importance of including representative samples in studies of child cognitive development.
5. Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry

   https://doi.org/10.1073/pnas.1910842117  

   Sylvester, Chad M. ; Yu, Qiongru ; Srivastava, A. Benjamin ; Marek, Scott ; Zheng, Annie ; Alexopoulos, Dimitrios ; Smyser, Christopher D. ; Shimony, Joshua S. ; Ortega, Mario ; Dierker, Donna L. ; et al ( February 2020 , Proceedings of the National Academy of Sciences)

   The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brainmore »networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala–cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

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