Functional Connectivity In Individuals Research Articles

Longitudinal stability of individual brain plasticity patterns in blindness

The primary visual cortex (V1) in blindness is engaged in a wide spectrum of tasks and sensory modalities, including audition, touch, language, and memory. This widespread involvement raises questions regarding the constancy of its role and whether it might exhibit flexibility in its function over time, connecting to diverse network functions specific to task demands. This would suggest that reorganized V1 assumes a role like multiple-demand system regions. Alternatively, varying patterns of plasticity in blind V1 may be attributed to individual factors, with different blind individuals recruiting V1 preferentially for different functions. In support of this, we recently showed that V1 functional connectivity (FC) varies greatly across blind individuals. But do these represent stable individual patterns of plasticity, or are they driven more by instantaneous changes, like a multiple-demand system now inhabiting V1? Here, we tested whether individual FC patterns from the V1 of blind individuals are stable over time. We show that over two years, FC from the V1 is unique and highly stable in a small sample of repeatedly sampled congenitally blind individuals. Further, using multivoxel pattern analysis, we demonstrate that the unique reorganization patterns of these individuals allow decoding of participant identity. Together with recent evidence for substantial individual differences in V1 connectivity, this indicates that there may be a consistent role for V1 in blindness, which may differ for each individual. Further, it suggests that the variability in visual reorganization in blindness across individuals could be used to seek stable neuromarkers for sight rehabilitation and assistive approaches.

Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study.

Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Can structure predict function at individual level in the human connectome?

Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the methods are not appropriate for the data, that the sample size is too small, or that the data does not contain sufficient information to learn a mapping from SC to FC. We advise future individual-level studies to explicitly report results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.

Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy.

Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.

Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility.

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

Early prediction of dementia using fMRI data with a graph convolutional network approach

Objective. Alzheimer’s disease is a progressive neurodegenerative dementia that poses a significant global health threat. It is imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions to prevent further deterioration of dementia. This study focuses on the early prediction of dementia utilizing Magnetic Resonance Imaging (MRI) data, using the proposed Graph Convolutional Networks (GCNs). Approach. Specifically, we developed a functional connectivity (FC) based GCN framework for binary classifications using resting-state fMRI data. We explored different types and processing methods of FC and evaluated the performance on the OASIS-3 dataset. We developed the GCN model for two different purposes: (1) MCI diagnosis: classifying MCI from normal controls (NCs); and (2) dementia risk prediction: classifying NCs from subjects who have the potential for developing MCI but have not been clinically diagnosed as MCI. Main results. The results of the experiments revealed several important findings: First, the proposed GCN outperformed both the baseline GCN and Support Vector Machine (SVM). It achieved the best average accuracy of 80.3% (11.7% higher than the baseline GCN and 23.5% higher than SVM) and the highest accuracy of 91.2%. Secondly, the GCN framework with (absolute) individual FC performed slightly better than that with global FC generally. However, GCN using global graphs with appropriate connectivity can achieve equivalent or superior performance to individual graphs in some cases, which highlights the significance of suitable connectivity for achieving performance. Additionally, the results indicate that the self-network connectivity of specific brain network regions (such as default mode network, visual network, ventral attention network and somatomotor network) may play a more significant role in GCN classification. Significance. Overall, this study offers valuable insights into the application of GCNs in brain analysis and early diagnosis of dementia. This contributes significantly to the understanding of MCI and has substantial potential for clinical applications in early diagnosis and intervention for dementia and other neurodegenerative diseases. Our code for GCN implementation is available at: https://github.com/Shuning-Han/FC-based-GCN.

Intrinsic Functional Connectivity between the Anterior Insular and Retrosplenial Cortex as a Moderator and Consequence of Cocaine Self-Administration in Rats.

While functional brain imaging studies in humans suggest that chronic cocaine use alters functional connectivity (FC) within and between key large-scale brain networks, including the default mode network (DMN), the salience network (SN), and the central executive network (CEN), cross-sectional studies in humans are challenging to obtain brain FC prior to cocaine use. Such information is critical to reveal the relationship between individual's brain FC and the subsequent development of cocaine dependence and brain changes during abstinence. Here, we performed a longitudinal study examining functional magnetic resonance imaging (fMRI) data in male rats (n = 7), acquired before cocaine self-administration (baseline), on 1 d of abstinence following 10 d of cocaine self-administration, and again after 30 d of experimenter-imposed abstinence. Using repeated-measures analysis of variance (ANOVA) with network-based statistics (NBS), significant connectivity changes were found between anterior insular cortex (AI) of the SN, retrosplenial cortex (RSC) of the DMN, somatosensory cortex, and caudate-putamen (CPu), with AI-RSC FC showing the most robust changes between baseline and 1 d of abstinence. Additionally, the level of escalated cocaine intake is associated with AI-RSC and AI-CPu FC changes between 1 d and 30 d of abstinence; further, the subjects' AI-RSC FC prior to cocaine intake is a significant moderator for the AI-RSC changes during abstinence. These results provide novel insights into the roles of AI-RSC FC before and after cocaine intake and suggest this circuit to be a potential target to modulate large-scale network and associated behavioral changes in cocaine use disorders.

Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the "fingerprint of FC" (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Longitudinal robustness of resting‐state functional connectivity signatures across different stages of the Alzheimer’s disease continuum

AbstractBackgroundA growing number of resting‐state fMRI (rs‐fMRI) studies report changes in network activity to be a frequent and often early pathological feature in many neurodegenerative conditions. The majority of studies have primarily focused on changes in group averages, failing to account for individual functional connectivity (FC) idiosyncrasies that have been shown to provide a unique identifier or “fingerprint” in healthy individuals (Finn et al., 2015). It thus remains unclear to what extent FC‐signatures are stable in the context of cognitive decline and Alzheimer’s Disease (AD) pathology (e.g., β‐amyloid [Aβ] and tau). We studied the robustness of subject‐specific FC in a longitudinal cohort covering the AD spectrum.Method275 participants with longitudinal rs‐fMRI data—baseline plus a follow up after approximately two‐years—were selected from the Swedish BioFINDER‐2 cohort, split into six groups: cognitively normal (CN) Aβ‐, subjective cognitive decline (SCD) Aβ‐, SCD Aβ+, mild cognitive decline (MCI) Aβ‐, MCI Aβ+, and AD Aβ+. FC matrices were derived from each subject rs‐fMRI session using the Schaefer cortical parcellation (200 and 400 ROIs), and consequently, a 275×275 identifiability matrix (IM) was derived; see Fig. 1B. For each subject, two fingerprinting measures were extracted from the IM: self identifiability (I‐self) and differential identifiability (I‐diff); see Fig.1B.ResultI‐self and I‐diff show varied distributions across the six groups, but values remain notably high even within the AD Aβ+ (Fig.2A). Both I‐self and I‐diff show significant differences between CN Aβ‐ and AD Aβ+ (Fig.2B). SCD Aβ‐ not only show a greater difference to AD Aβ+, but also to SCD Aβ+ and MCI Aβ+; see Fig.2B. Baseline tau‐PET was associated with I‐self (r = ‐0.1928,p = 0.0014) and to a lesser degree with I‐diff (r = ‐0.1293, p = 0.0334), whereas baseline MMSE was only found associated to I‐self (r = 0.1671,p = 0.0055); neither I‐self nor I‐diff were associated with age (Fig.3).ConclusionOur results suggest that resting‐state FC is mostly stable under cognitive decline and presence of AD pathology. FC was most notably differentiable for SCD Aβ‐, suggesting it as a potential signature to decipher underlying cognitive status and to predict likelihood of future conversion to AD.

Effects of sling exercises on pain, function, and corticomuscular functional connectivity in individuals with chronic low back pain- preliminary study.

Individuals with chronic low back pain (CLBP) exhibit altered brain function and trunk muscle activation. This study examined the effects of sling exercises on pain, function, and corticomuscular coherence (CMC) in healthy adults and individuals with CLBP. Eight individuals with CLBP and 15 healthy adults received sling exercise training for 6 weeks. Before and after training, participants performed two motor tasks: rapid arm lifts and repeated trunk flexion-extension tasks, and electromyography of the trunk muscles and electroencephalography of the sensorimotor cortex were recorded. Chi-squared test and Mann-Whitney U tests were used for between group comparison, and Wilcoxon signed-rank tests were used for pre- and post-training comparison. Spearman's Rank Correlation Coefficient (Rs) was used to identify for the relationship between motor performance and Corticomuscular coherence. Sling exercises significantly improved pain (median from 3 to 1, p = .01) and Oswestry Disability Index scores (median from 2.5 to 2, p = .03) in the CLBP group. During rapid arm lifts, individuals with CLBP showed lower beta CMC of the transverse abdominis and internal oblique (Tra/IO) (0.8 vs. 0.49, p = .01) and lumbar erector spinae (0.70 vs. 0.38, p = .04) than the control group at baseline. During trunk flexion-extension, the CLBP group showed higher gamma CMC of the left Tra/IO than the control group at baseline (0.28 vs. 0.16 , p = .001). After training, all CMC became statistically non-significant between groups. The training induced improvement in anticipatory activation of the Tra/IO was positively correlated with the beta CMC (rs = 0.7851, p = .02). A 6-week sling exercises diminished pain and disability in patients with CLBP and improved the anticipatory activation and CMC in some trunk muscles. These improvements were associated with training induced changes in corticomuscular connectivity in individuals with CLBP.

Frontoparietal functional connectivity moderates the link between time spent on social media and subsequent negative affect in daily life

Evidence on the harms and benefits of social media use is mixed, in part because the effects of social media on well-being depend on a variety of individual difference moderators. Here, we explored potential neural moderators of the link between time spent on social media and subsequent negative affect. We specifically focused on the strength of correlation among brain regions within the frontoparietal system, previously associated with the top-down cognitive control of attention and emotion. Participants (N = 54) underwent a resting state functional magnetic resonance imaging scan. Participants then completed 28 days of ecological momentary assessment and answered questions about social media use and negative affect, twice a day. Participants who spent more than their typical amount of time on social media since the previous time point reported feeling more negative at the present moment. This within-person temporal association between social media use and negative affect was mainly driven by individuals with lower resting state functional connectivity within the frontoparietal system. By contrast, time spent on social media did not predict subsequent affect for individuals with higher frontoparietal functional connectivity. Our results highlight the moderating role of individual functional neural connectivity in the relationship between social media and affect.

Individual resting-state network functional connectivity predicts treatment improvement of repetitive transcranial magnetic stimulation in major depressive disorder: A pilot study

The current pilot study aimed to exploratively investigate whether individual functional connectivity (FC) of the rTMS stimulation site with resting-state networks could predict the individual efficacy of rTMS treatment. We found that rTMS induced an increase of the FC between the stimulation site and the limbic network (LN) in healthy participants, and that this individualized FC was negatively correlated with the rTMS treatment improvement in MDD patients. Moreover, the LN successfully guided the personalized rTMS therapy. These findings highlighted the crucial role of the LN in understanding the mechanisms underlying rTMS treatment improvement, and the personalized therapy in MDD patients.

Regional cerebral metabolism alterations and functional connectivity in individuals with opioid use disorder: An integrated resting-state PET/fMRI study

Individuals with opioid use disorder (OUD) have been reported to show abnormal brain metabolism and impaired coupling among brain networks such as the default mode network (DMN), salience network (SN), and executive control network (ECN). However, the characteristics of brain glucose metabolism and its related functions in the brain networks in individuals with OUD remain unknown. Thirty-six individuals with OUD and thirty matched healthy controls (HCs) were recruited in this integrated positron emission tomography/magnetic resonance imaging (PET/MRI) study. Differences in glucose metabolism were analyzed by using 18F-fluorodeoxyglucose (18F-FDG), and the corresponding coupling characteristics of the individuals with OUD were also analyzed. The individuals with OUD showed widespread bilateral hypometabolism in the middle temporal gyrus (MTG), superior temporal gyrus, angular gyrus, supramarginal gyrus, inferior parietal lobe, Rolandic operculum, and left insula, but obvious hypermetabolism in the brainstem and left cerebellum. Meanwhile, in individuals with OUD, the hypometabolism of right MTG which is included in the DMN was accompanied by decreased coupling with the left superior frontal gyrus and right superior parietal gyrus which are included in the ECN. Furthermore, individuals with OUD showed a positive correlation between the duration of heroin use and glucose metabolism of the left MTG. The individuals with OUD were characterized by widespread bilateral hypometabolism in the temporal and parietal regions but obvious hypermetabolism in the brainstem and left cerebellum. The results suggest that the hypometabolism in the temporal and parietal regions might be related to DMN dysfunction and the hypermetabolism in the brainstem and left cerebellum may be compensate for other brain regions showing hypometabolism. In particular, hypometabolism in the self-referential-related DMN regions in OUD might attenuate their relationships with the inhibitory-control-related ECN regions. These findings highlight the importance of evaluating the metabolic and functional profiles of the right MTG in future studies on the treatment of OUD.

Effects of a dissociative drug on fronto-limbic resting-state functional connectivity in individuals with posttraumatic stress disorder: a randomized controlled pilot study

RationaleA subanesthetic dose of ketamine, a non-competitive N-methyl-D-aspartate glutamate receptor (NMDAR) antagonist, elicits dissociation in individuals with posttraumatic stress disorder (PTSD), who also often suffer from chronic dissociative symptoms in daily life. These debilitating symptoms have not only been linked to worse PTSD trajectories, but also to increased resting-state functional connectivity (RSFC) between medial prefrontal cortex (mPFC) and amygdala, supporting the conceptualization of dissociation as emotion overmodulation. Yet, as studies were observational, causal evidence is lacking.ObjectivesThe present randomized controlled pilot study examines the effect of ketamine, a dissociative drug, on RSFC between mPFC subregions and amygdala in individuals with PTSD.MethodsTwenty-six individuals with PTSD received either ketamine (0.5mg/kg; n = 12) or the control drug midazolam (0.045mg/kg; n = 14) during functional magnetic resonance imaging (fMRI). RSFC between amygdala and mPFC subregions, i.e., ventromedial PFC (vmPFC), dorsomedial PFC (dmPFC) and anterior-medial PFC (amPFC), was assessed at baseline and during intravenous drug infusion.ResultsContrary to pre-registered predictions, ketamine did not promote a greater increase in RSFC between amygdala and mPFC subregions from baseline to infusion compared to midazolam. Instead, ketamine elicited a stronger transient decrease in vmPFC-amygdala RSFC compared to midazolam.ConclusionsA dissociative drug did not increase fronto-limbic RSFC in individuals with PTSD. These preliminary experimental findings contrast with prior correlative findings and call for further exploration and, potentially, a more differentiated view on the neurobiological underpinning of dissociative phenomena in PTSD.

Structural and functional connectome relationships in early childhood

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual’s functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.

Personalized, parcel-guided rTMS for the treatment of major depressive disorder: Safety and proof of concept.

Not all patients with major depressive disorder (MDD) benefit from the US Food and Drug Administration-approved use of repetitive transcranial magnetic stimulation (rTMS) at the dorsolateral prefrontal cortex. We may be undertreating depression with this one-size-fits-all rTMS strategy. We present a retrospective review of targeted and connectome-guided rTMS in 26 patients from Cingulum Health from 2020 to 2023 with MDD or MDD with associated symptoms. rTMS was conducted by identifying multiple cortical targets based on anomalies in individual functional connectivity networks as determined by machine learning connectomic software. Quality of lifeassessed by the EuroQol (EQ-5D) scoreand depression symptomsassessed by the Beck Depression Inventory (BDI) were administered prior to treatment, directly after, and at a follow-up consultation. Of the 26 patients treated with rTMS, 16 (62%) attained remission after treatment. Of the 19 patients who completed follow-up assessments after an average interval of 2.6 months, 11 (58%) responded to treatment and 13 (68%) showed significant remission. Between patients classified with or without treatment-resistant depression, there was no difference in BDI improvement. Additionally, there was significant improvement in quality of life after treatment and during follow-up compared to baseline. This review is retrospective in nature, so there is no control group to assess the placebo effect on patient outcomes. The personalized, connectome-guided approach of rTMS is safe and may be effective for depression. This personalized rTMS treatment allows for co-treatment of multiple disorders, such as the comorbidity of depression and anxiety.

Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.

Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.

Predicting executive functioning from brain networks: modality specificity and age effects.

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2< 0.07, r< 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain

Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.