Connectivity Patterns Research Articles

Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders

BackgroundNeuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches searching for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores. MethodsThe polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity (FC) mirrors the whole-brain circuitry characteristics of a trait. We computed PCS for eight neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and three neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson’s disease) across 22 datasets with resting-state functional MRI of 10,667 individuals (5,325 patients, 5,342 controls). We further examined PCS in 26,673 individuals from the population-based UK Biobank cohort. ResultsPCS was consistently higher in out-of-sample patients across six of the eight neuropsychiatric and across all three investigated neurodegenerative disorders ([min, max]: AUC = [0.55, 0.73], pFDR = [1.8 x 10-16, 4.5 x 10-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (pFDR < 9.7 x 10-5), lower cognitive performance (pFDR < 5.3 x 10-5), and lower general wellbeing (pFDR < 9.7 x 10-4). ConclusionsOur findings reveal generalizable whole-brain connectivity alterations in brain disorders. PCS effectively aggregates disorder-related signatures across the entire brain into an interpretable, subject-specific metric. A toolbox is provided for PCS computation.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples.

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach

IntroductionSex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN). MethodsUsing an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns. ResultsOur findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns. ConclusionThis study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Thalamus and its functional connections with cortical regions contribute to complexity-dependent cognitive reasoning

The thalamus is crucial for supporting various cognitive behaviors due to its extensive connectivity with multiple cortical regions. However, the role of the thalamus and its functional connections with cortical regions in cognitive reasoning remains unclear, since previous research has mainly focused on cortical regions when studying the neural mechanisms underlying cognitive reasoning. To fill this knowledge gap, we utilized 7 T functional magnetic resonance imaging (fMRI) to study the activation patterns of the thalamus and its functional connections with cortical regions during cognitive reasoning task, while also examining how the complexity of reasoning tasks affects thalamic activation and functional connections with cortical regions. Our findings showed that cognitive reasoning processes are related to increased activation of the thalamus and its functional connections with a specific set of cortical regions, consisting of dorsolateral prefrontal cortex, inferior frontal sulcus, intraparietal sulcus, anterior cingulate cortex/presupplementary motor area, precuneus, and ventral medial prefrontal cortex. Moreover, the increase in relational complexity of the reasoning tasks led to a corresponding increase in thalamic activation and functional connectivity with cortical regions. Given the complex thalamus structure, including multiple distinct nuclei exhibiting specific functional connections with particular cortical regions, we used an atlas defined thalamic subdivisions based on its structural connectivity with different cortical regions. Our findings indicated that these different thalamic subregions not only exhibited distinct connectivity patterns with specific cortical regions during performance of cognitive reasoning, but also showed distinct connectivity patterns varied with task complexity. Overall, our study presents evidence of the thalamus’s role and its connections with cortical regions in supporting increasingly complex cognitive reasoning behavior, illuminating its contribution to higher-order cognitive functions, such as reasoning.

The contribution of nearshore oceanography to temporal variation in larval dispersal.

Patterns of population connectivity shape ecological and evolutionary phenomena from population persistence to local adaptation and can inform conservation strategy. Connectivity patterns emerge from the interaction of individual behavior with a complex and heterogeneous environment. Despite ample observation that dispersal patterns vary through time, the extent to which variation in the physical environment can explain emergent connectivity variation is not clear. Empirical studies of its contribution promise to illuminate a potential source of variability that shapes the dynamics of natural populations. We leveraged simultaneous direct dispersal observations and oceanographic transport simulations of the clownfish Amphiprion clarkii in the Camotes Sea, Philippines, to assess the contribution of oceanographic variability to emergent variation in connectivity. We found that time-varying oceanographic simulations on both annual and monsoonal timescales partly explained the observed dispersal patterns, suggesting that temporal variation in oceanographic transport shapes connectivity variation on these timescales. However, interannual variation in observed mean dispersal distance was nearly 10 times the expected variation from biophysical simulations, revealing that additional biotic and abiotic factors contribute to interannual connectivity variation. Simulated dispersal kernels also predicted a smaller scale of dispersal than the observations, supporting the hypothesis that undocumented abiotic factors and behaviors such as swimming and navigation enhance the probability of successful dispersal away from, as opposed to retention near, natal sites. Our findings highlight the potential for coincident observations and biophysical simulations to test dispersal hypotheses and the influence of temporal variability on metapopulation persistence, local adaptation, and other population processes.

The shared neurobiological basis of developmental dyslexia and developmental stuttering: A meta-analysis of functional and structural MRI studies

BackgroundDevelopmental dyslexia (DD) and persistent developmental stuttering (PDS) are the most representative written and spoken language disorders, respectively, and both significantly hinder life success. Although widespread brain alterations are evident in both DD and PDS, it remains unclear to what extent these two language disorders share common neural substrates. MethodsA systematic review and meta-analysis of task-based functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) studies of PDS and DD were conducted to explore the shared functional and anatomical alterations across these disorders. ResultsThe results of fMRI studies indicated shared hypoactivation in the left inferior temporal gyrus and inferior parietal gyrus across PDS and DD compared to healthy controls. When examined separately for children and adults, we found that child participants exhibited reduced activation in the left inferior temporal gyrus, inferior parietal gyrus, precentral gyrus, middle temporal gyrus, and inferior frontal gyrus, possibly reflecting the universal causes of written and spoken language disorders. In contrast, adult participants exhibited hyperactivation in the right precentral gyrus and left cingulate motor cortex, possibly reflecting common compensatory mechanisms. Anatomically, the analysis of VBM studies revealed decreased gray matter volume in the left inferior frontal gyrus across DD and PDS, which was exclusively observed in children. Finally, meta-analytic connectivity modeling and brain-behavior correlation analyses were conducted to explore functional connectivity patterns and related cognitive functions of the brain regions commonly involved in DD and PDS. ConclusionsThis study identified concordances in brain abnormalities across DD and PDS, suggesting common neural substrates for written and spoken language disorders and providing new insights into the transdiagnostic neural signatures of language disorders.

A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization.

Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

BackgroundPatients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.MethodologyIn a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.ResultsFC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.ConclusionsDifferences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection.

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog.

The role of mesoscale-driven connectivity patterns in coral recovery around Moorea and Tahiti, French Polynesia

Coral reefs are declining due to anthropogenic warming. Nonetheless, some have recovered quickly from repeated bleaching events. Coral recovery depends on adaptation capabilities, fishing pressure, overall number of stressors, reef conditions before the event, and degree of connectivity. Coral reefs that are connected to many others can receive viable larvae and regain coverage faster. Around Moorea and Tahiti, within the Society Islands of French Polynesia, coral cover has regained its previous levels rapidly, despite several mass bleaching events over the past three decades. Here it is explored whether the connectivity with distant reefs may support such recovery by modeling the transport of coral larvae around the islands over 28 years. Ocean currents enable connectivity with the Tuamotu Islands, ~ 250 km to the northeast, that act as sources to Moorea and Tahiti for pelagic larval durations of three weeks or longer. The circulation around Moorea and Tahiti is very dynamic; mesoscale eddies can also halt the connectivity with the Tuamotu Islands and sporadically transport larvae from reefs to the west and southeast instead. With many undisturbed coral reefs within a 300 km radius and strong mesoscale variability, a dynamic, long-range connectivity may explain the recovery of reefs around Moorea and Tahiti.

Subjective cognitive decline is associated with altered patterns of brain activity and connectivity during performance of an old/new recognition memory task

IntroductionSubjective Cognitive Decline (SCD) is considered a preclinical stage within the AD continuum. Knowledge about the functional changes in the brain associated with episodic memory retrieval and novelty recognition in people with SCD is currently very limited. MethodThe study aimed to evaluate behavioural and neurofunctional changes in individuals with SCD, measured relative to a control group, during successful episodic memory retrieval and novelty recognition, as well as to compare the functional connectivity patterns related to these cognitive processes within the Default Mode Network (DMN) in both groups. Participants performed an old/new recognition memory task with words while the BOLD signal was acquired. ResultsNo between-group differences were observed in the performance of the episodic memory task. However, during the successful recognition of old words, the SCD group showed brain hypoactivity in the right rolandic operculum and reduced functional connectivity between the DMN and the fronto-parietal control network (FPCN). During the correct identification of new words, the SCD group also showed reduced connectivity between the DMN and the FPCN, and lower connectivity within the DMN. ConclusionDespite the absence of objective evidence of cognitive impairment, people with SCD display several changes in brain activity and connectivity associated with episodic memory retrieval and novelty recognition.

Improving community detection in blockmodel by distance-based observation selection

Community detection is an important research topic in complex systems and has plenty of applications in real-world networks. Probabilistic methods, such as the Expectation–Maximization (EM), are developed to classify nodes that have similar connection patterns in a network based on blockmodels. However, the detection procedures in these models are typically started from randomly generated initial community distributions without prior knowledge. In biological and social networks, there are practical measures to obtain prior knowledge for a subset of nodes, such as local observations. These facts lead us to question how we can select a subset of nodes with known community labels to enhance the accuracy of the EM method. The current selection methods lack the relationship between detection accuracy and structural characteristics and most approaches consider the nodes as the center of communities, which is not suitable for block models. In this paper, we first study the relationships between the structural distance and detection accuracy without prior knowledge. Then we propose a distance-based indicator to describe the performance of the observation node set in the EM method. Finally, we introduce a scoring method based on the indicator to select a partial observation set, improving the accuracy of community detection using the EM method. Empirical results from synthetic and real-world networks corroborate that the proposed indicator could contribute to a better performance in kinds of scenarios.

The underlying mechanisms of alignment in error backpropagation through arbitrary weights

Understanding the mechanisms by which plasticity in millions of synapses in the brain is orchestrated to achieve behavioral and cognitive goals is a fundamental question in neuroscience. In this regard, insights from learning methods in artificial neural networks (ANNs) and in particular supervised learning using backpropagation (BP) seem inspiring. However, the implementation of BP requires exact matching between forward and backward weights, which is unrealistic given the known connectivity pattern in the brain (known as the “weight transport problem”). Notably, it has been shown that under certain conditions, error BackPropagation Through Arbitrary Weights (BP-TAW) can lead to a partial alignment between forward and backward weights (weight alignment or WA). This learning algorithm, which is also known as feedback alignment (FA), can result in surprisingly good degrees of accuracy in simple classification tasks. However, the underlying mechanisms and mathematical basis of WA are not thoroughly understood. In this work, we demonstrate the mathematical basis of WA and answer the question of why and in what conditions WA occurs. We show that the occurrence of WA in ANNs is induced by statistical properties of the output and error signals of neurons, such as autocorrelation and cross-correlation, and can happen even in the absence of learning or reduction of the loss function. Moreover, we show that WA can be improved significantly by limiting the norm of input weights to neurons and that such a weight normalization (WN) method can improve the classification accuracy of BP-TAW. The findings presented can be used to further improve the performance of BP-TAW and open new ways for exploring possible learning mechanisms in biological neural networks without exact matching between forward and backward weights.

Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain

Brain activity is characterized by brainwide spatiotemporal patterns that emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to recordings of whole-brain activity at single-neuron resolution and, therefore, allows the study of how large-scale brain dynamics emerge from local activity. In this study, we use a statistical mechanics approach—the pairwise maximum entropy model—to infer microscopic network features from collective patterns of activity in the larval zebrafish brain and relate these features to the emergence of observed whole-brain dynamics. Our findings indicate that the pairwise interactions between neural populations and their intrinsic activity states are sufficient to explain observed whole-brain dynamics. In fact, the pairwise relationships between neuronal populations estimated with the maximum entropy model strongly correspond to observed structural connectivity patterns. Model simulations also demonstrated how tuning pairwise neuronal interactions drives transitions between observed physiological regimes and pathologically hyperexcitable whole-brain regimes. Finally, we use virtual resection to identify the brain structures that are important for maintaining the brain in a physiological dynamic regime. Together, our results indicate that whole-brain activity emerges from a complex dynamical system that transitions between basins of attraction whose strength and topology depend on the connectivity between brain areas. Published by the American Physical Society 2024

Ecological connectivity of the Qiongzhou Strait: a case form Orangefin Ponyfish (Photopectoralis bindus) haplotype diversity and genetic structure

Grasping the genetic structure of marine fish populations is vital for comprehending species connectivity patterns and determining the appropriate spatiotemporal scales for conservation management strategies. Here, we analyzed the population genetics of the Orangefin Ponyfish (Photopectoralis bindus Valenciennes, 1835) by examining a portion of the gene coding for the mitochondrial cytochrome c oxidase subunit I. The aim was to evaluate the haplotype pattern, genetic structure, demographic history, as well as the influence of ecological connectivity through the Qiongzhou Strait on the distribution patterns of this species in the northern South China Sea and the Beibu Gulf. In total, 257 specimens yielded only 13 haplotypes, with the predominant haplotype present at all sampling locations. The analysis revealed a “star-like” haplotype pattern, indicating low levels of both haplotype and nucleotide diversity. Additionally, a small but significant genetic structure was observed between the coastal regions flanking the Leizhou Peninsula. These patterns in the haplotype network and genetic structure may be significantly influenced by contemporary currents, particularly through the connectivity of the Qiongzhou Strait. Tajima’s D and Fu’s Fs demonstrated pronouncedly negative values, along with a unimodal mismatch distribution, suggested a recent demographic expansion of Photopectoralis bindus during the late Pleistocene, likely influenced by fluctuations in sea levels.

A shared spatial topography links the functional connectome correlates of cocaine use disorder and dopamine D2/3 receptor densities

The biological mechanisms that contribute to cocaine and other substance use disorders involve an array of cortical and subcortical systems. Prior work on the development and maintenance of substance use has largely focused on cortico-striatal circuits, with relatively less attention on alterations within and across large-scale functional brain networks, and associated aspects of the dopamine system. Here, we characterize patterns of functional connectivity in cocaine use disorder and their spatial association with neurotransmitter receptor densities and transporter bindings assessed through PET. Profiles of functional connectivity in cocaine use disorder reliably linked with spatial densities of dopamine D2/3 receptors across independent datasets. These findings demonstrate that the topography of dopamine receptor densities may underlie patterns of functional connectivity in cocaine use disorder, as assessed through fMRI.

Investigating Functional Connectivity in Adolescent Depression and Suicide Attempt during Neurofeedback Sessions: A Multivariate Random Covariance Model Approach

Background A recent neurofeedback functional magnetic resonance imaging (NF, fMRI) study on depressed vs. healthy adolescents elicited differential functional connectivity (FC) amongst brain regions of interest (ROIs). Previous results employed univariate methods and included only two seed areas of FC (amygdala and hippocampus). In this study, we propose a new multivariate analysis for whole-network FC estimation. Methods Primary analyses concerned a pre-identified network of 17 salient ROIs reflecting key regions in self-processing and emotion regulation. A random covariance model (RCM) was applied to jointly estimate participant- and group-specific connectivity, where FC was measured by partial correlation conditioned on or adjusted for rest-of-network connectivity patterns. Secondary analyses concerned participant-specific network association with mental functioning changes and the AAL3 whole-brain atlas. Results New findings suggested that depressed adolescents with a suicide attempt expressed significantly higher positive FC between the left temporal gyrus and the left amygdala during NF, compared to negative FC in non-attempting depressed youth, while healthy controls displayed negative FC between the insula, inferior frontal gyrus to inferior parietal lobe connection, compared to mild negative connectivity in depressed adolescents. Previous cross-hemispheric findings in depressed vs. healthy adolescents were corroborated. Conclusion A multivariate RCM uncovered key ROI-pairwise connections differentiating FC patterns between depressed youth vs. healthy controls and among depressed youth, with and without a suicide attempt. Findings were strengthened by enhanced inference vs. univariate methods, and corroboration of previous NF secondary analyses demonstrated future utility for participant-specific study in association with clinical outcomes and/or whole-brain analyses with larger sample sizes.

Unearthing the hidden links: Investigating the functional connectivity between amygdala subregions and brain networks in bipolar disorder through resting-state fMRI

IntroductionBipolar disorder is a multifaceted psychiatric condition characterized by fluctuating activity levels and dysfunctional mood states, oscillating between manic and depressive episodes. These mood disturbances are accompanied by persistent functional and cognitive impairments, even during periods of euthymia. Prior studies have underscored the critical role of amygdala activity in the pathophysiology of bipolar disorder. This research aims to utilize resting-state functional Magnetic Resonance Imaging (rs-fMRI) to explore the functional modifications in the six sub-regions that compose the amygdala of individuals diagnosed with bipolar disorder. MethodThe study encompassed 80 participants, bifurcated into two groups: 40 individuals with bipolar disorder and 40 healthy controls. Each group comprised an equal gender distribution of 20 females and 20 males, ranging in age from 21 to 50 years. Using rs-fMRI, we examined the functional connectivity within six amygdala sub-regions across eight regional functional networks. ResultsComparative analysis between the control group and the bipolar patients revealed that all six amygdala sub-regions demonstrated connectivity with the eight functional brain networks. Notable similarities and disparities were observed in the connectivity patterns between the bipolar group and controls, particularly within the amygdala's sub-regions and other brain networks. The most significant functional connectivity alterations were found with the salience network and the default mode network. Additionally, alterations in the functional connectivity between the amygdala, sensory-motor, and visual networks were noted in bipolar patients. ConclusionThe study's findings highlight the distinct patterns of resting-state functional connectivity of the amygdala and various brain networks in differentiating bipolar patients from healthy controls. These variations suggest the existence of multiple pathophysiological mechanisms contributing to emotional dysregulation in bipolar disorder.

Divergent pattern of functional connectivity within the dorsal attention network differentiates schizophrenia and bipolar disorder patients.

Schizophrenia (SZ) and bipolar disorder (BD) share common clinical features, symptoms, and neurocognitive deficits, which results in common misdiagnosis. Recently, it has been suggested that alterations within brain networks associated with perceptual organization yield potential to distinguish SZ and BD individuals. The aim of our study was to evaluate whether functional connectivity (FC) of the dorsal attention network (DAN) may differentiate both conditions. The study involved 90 participants: 30 remitted SZ patients, 30 euthymic BD patients, and 30 healthy controls (HC). Resting state functional magnetic resonance imaging was used to compare the groups in terms of the FC within the core nodes of the DAN involving frontal eye fields (FEF) and intraparietal sulcus (IPS). BD patients presented weaker inter-hemispheric FC between right and left FEF than HC. While SZ did not differ from HC in terms of inter-FEF connectivity, they presented increased inter- and intra-hemispheric FC between FEF and IPS. When compared with BD, SZ patients showed increased FC between right FEF and other nodes of the network (bilateral IPS and left FEF). We have shown that altered resting state FC within DAN differentiates BD, SZ, and HC groups. Divergent pattern of FC within DAN, consisting of hypoconnectivity in BD and hyperconnectivity in SZ, might yield a candidate biomarker for differential diagnosis between both conditions. More highly powered studies are needed to confirm these possibilities.