State fMRI Research Articles

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain’s functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

Resting state functional connectivity as a predictor of brief intervention response in adults with alcohol use disorder: Apreliminary study.

Brief interventions for alcohol use disorder (AUD) are generally efficacious, albeit with variability in response. Resting state functional connectivity (rsFC) may characterize neurobiological indicators that predict the response to brief interventions and is the focus of the current investigation. Forty-six individuals with AUD (65.2% female) completed a resting state functional magnetic resonance imaging (fMRI) scan immediately followed by a brief intervention aimed at reducing alcohol consumption. Positive clinical response was defined as a reduction in alcohol consumption by at least one World Health Organization (WHO) risk drinking level at 3-month follow-up. rsFC was analyzed using seed-to-voxel analysis with seed regions from four networks: salience network, reward network, frontoparietal network, and default mode network. At baseline, responders had greater rsFC between the following seed regions in relation to voxel-based clusters than non-responders: (i) anterior cingulate cortex (ACC) in relation to left postcentral gyrus and right supramarginal gyrus (salience network); (ii) right posterior parietal cortex in relation to right ventral ACC (salience network); (iii) right interior frontal gyrus (IFG) pars opercularis in relation to right cerebellum and right occipital fusiform gyrus (frontoparietal); and (iv) right primary motor cortex in relation to left thalamus (default mode). Lower rsFC in responders vs. nonresponders was seen between the (i) right rostral prefrontal cortex in relation to left IFG pars triangularis (frontoparietal); (ii) right IFG pars triangularis in relation to right cerebellum (frontoparietal); (iii) right IFG pars triangularis in relation to right frontal eye fields and right angular gyrus (frontoparietal); and (iv) right nucleus accumbens in relation to right orbital frontal cortex and right insula (reward). Resting state functional connectivity in the frontoparietal, salience, and reward networks predicts the response to a brief intervention in individuals with AUD and could reflect greater receptivity or motivation for behavior change.

Disrupted network communication predicts mild cognitive impairment in end-stage renal disease: an individualized machine learning study based on resting-state fMRI.

End-Stage Renal Disease (ESRD) is known to be associated with a range of brain injuries, including cognitive decline. The purpose of this study is to investigate the functional connectivity (FC) of the resting-state networks (RSNs) through resting state functional magnetic resonance imaging (MRI), in order to gain insight into the neuropathological mechanism of ESRD. A total of 48 ESRD patients and 49 healthy controls underwent resting-state functional MRI and neuropsychological tests, for which Independent Components Analysis and graph-theory (GT) analysis were utilized. With the machine learning results, we examined the connections between RSNs abnormalities and neuropsychological test scores. Combining intra/inter network FC differences and GT results, ESRD was optimally distinguished in the testing dataset, with a balanced accuracy of 0.917 and area under curve (AUC) of 0.942. Shapley additive explanations results revealed that the increased functional network connectivity between DMN and left frontoparietal network (LFPN) was the most critical predictor for ESRD associated mild cognitive impairment diagnosis. Moreover, hypoSN (salience network) was positively correlated with Attention scores, while hyperLFPN was negatively correlated with Execution scores, indicating correlations between functional disruption and cognitive impairment measurements in ESRD patients. This study demonstrated that both the loss of FC within the SN and compensatory FC within the lateral frontoparietal network coexist in ESRD. This provides a network basis for understanding the individual brain circuits and offers additional noninvasive evidence to comprehend the brain networks in ESRD.

A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis.

Resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) analysis has illuminated brain network interactions across many neuropsychiatric disorders. A common analysis approach involves using hard clustering methods to identify transitory states of brain activity, and in response to this, other methods have been developed to quantify the importance of specific dFNC interactions to identified states. Some of these methods involve perturbing individual features and examining the number of samples that switch states. However, only a minority of samples switch states. As such, these methods actually identify the importance of dFNC features to the clustering of a subset of samples rather than the overall clustering. In this study, we present a novel approach that more capably identifies the importance of each feature to the overall clustering. Our approach uses fuzzy clustering to output probabilities of each sample belonging to states and then measures their Kullback-Leibler divergence after perturbation. We show the viability of our approach in the context of schizophrenia (SZ) default mode network analysis, identifying significant differences in state dynamics between individuals with SZ and healthy controls. We further compare our approach with an existing approach, showing that it captures the effects of perturbation upon most samples. We also find that interactions between the posterior cingulate cortex (PCC) and the anterior cingulate cortex and the PCC and precuneus are important across methods. We expect that our novel explainable clustering approach will enable further progress in rs-fMRI analysis and to other clustering applications.

A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data.

Brain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global structural network. Researchers have been interested in using these models to explain measured brain activity, particularly resting state functional magnetic resonance imaging (rs-fMRI). BNMs have shown to produce similar properties as measured data computed over longer periods of time such as average functional connectivity (FC), but it is unclear how well simulated trajectories compare to empirical trajectories on a timepoint-by-timepoint basis. During task fMRI, the relevant processes pertaining to task occur over the time frame of the hemodynamic response function, and thus it is important to understand how BNMs capture these dynamics over these short periods. To test the nature of BNMs' short-term trajectories, we used a deep learning technique called Neural ODE to simulate short trajectories from estimated initial conditions based on observed fMRI measurements. To compare to previous methods, we solved for the parameterization of a specific BNM, the Firing Rate Model, using these short-term trajectories as a metric. Our results show an agreement between parameterization of using previous long-term metrics with the novel short term metrics exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity, and the presence of noise. Therefore, we conclude that there is evidence that by using Neural ODE, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.

Increased hippocampal efficiency is associated with greater headache frequency in adolescents with chronic headache.

Adults with chronic headache have altered brain hippocampal efficiency networks. Less is known about the mechanisms underlying chronic headache in youth. In total, 29 youth with chronic headache (10-18years), and 29 healthy, age- and sex-matched controls tracked their headache attacks daily for 1-month period. Following this, they underwent a resting state functional magnetic resonance imaging scan and self-reported on their pubertal status, post-traumatic stress, anxiety, and depression symptoms. Graph-based topological analyses of brain networks, rendering hippocampal efficiency values were performed. T-tests were used to compare hippocampal efficiency metrics between patients and controls. Linear regression was used to examine significant hippocampal efficiency metrics in relation to headache frequency in patients, controlling for age, sex, pubertal status, post-traumatic stress, anxiety, and depression symptoms. Patients had higher right hippocampal global efficiency, shorter right hippocampal path length, and higher right hippocampal clustering coefficient compared to controls (P < 0.05). Higher right hippocampal global efficiency, shorter right hippocampal path length, and higher right hippocampal clustering coefficients were positively associated with greater headache frequency (P < 0.05). The hippocampus is largely involved in memory formation and retrieval, and this data provides additional support for previous findings demonstrating the importance of the hippocampus and pain memories for the chronification of pain.

Neural mechanisms of emotional health in traumatic brain injury patients undergoing rTMS treatment

Emotional dysregulation such as that seen in depression, are a long-term consequence of mild traumatic brain injury (TBI), that can be improved by using neuromodulation treatments such as repetitive transcranial magnetic stimulation (rTMS). Previous studies provide insights into the changes in functional connectivity related to general emotional health after the application of rTMS procedures in patients with TBI. However, these studies provide little understanding of the underlying neuronal mechanisms that drive the improvement of the emotional health in these patients. The current study focuses on inferring the effective (causal) connectivity changes and their association with emotional health, after rTMS treatment of cognitive problems in TBI patients (N = 32). Specifically, we used resting state functional magnetic resonance imaging (fMRI) together with spectral dynamic causal model (spDCM) to investigate changes in brain effective connectivity, before and after the application of high frequency (10 Hz) rTMS over left dorsolateral prefrontal cortex. We investigated the effective connectivity of the cortico-limbic network comprised of 11 regions of interest (ROIs) which are part of the default mode, salience, and executive control networks, known to be implicated in emotional processing. The results indicate that overall, among extrinsic connections, the strength of excitatory connections decreased while that of inhibitory connections increased after the neuromodulation. The cardinal region in the analysis was dorsal anterior cingulate cortex (dACC) which is considered to be the most influenced during emotional health disorders. Our findings implicate the altered connectivity of dACC with left anterior insula and medial prefrontal cortex, after the application of rTMS, as a potential neural mechanism underlying improvement of emotional health. Our investigation highlights the importance of these brain regions as treatment targets in emotional processing in TBI.

Deep Diffusion Kalman Filter Combining Large-Scale Neuronal Networks Simulation with Multimodal Neuroimaging Data

Using computers to numerically simulate large-scale neuronal networks has become a common method for studying the mechanism of the human brain, and neuroimaging has brought forth multimodal brain data. Determining how to fully consider these multimodal data in the process of brain modeling has become a crucial issue. Data assimilation is an efficient method for combining the dynamic system with the observation data, and many related algorithms have been developed. In this paper, we utilize data assimilation to perform brain state variables estimation, put forward a general form of a diffusion Kalman filter named the deep diffusion Kalman filter, and provide a specific algorithm that is combined with data assimilation. Then, we theoretically demonstrate the deep diffusion Kalman filter’s effectiveness and further validate it by using an experiment in the toy model. Finally, according to the resting state functional magnetic resonance imaging signals, we assimilate a cortex networks model with the resting state brain, where the correlation is as high as 98.42%.

Altered regional brain activity and functional connectivity in resting-state brain networks associated with psychological erectile dysfunction.

Erectile dysfunction (ED), especially psychological ED (pED), is usually accompanied with psychological factors, which are related to abnormal activity in brain regions involved in sexual behavior. However, the mechanisms underlying functional changes in the brain of pED are still unclear. The present study aimed to explore the abnormalities of brain function, as well as their relationships with sexual behavior and emotion in pED patients. Resting state functional magnetic resonance imaging (rs-fMRI) data were collected from 31 pED patients to 31 healthy controls (HCs). The values of amplitude of fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) were calculated and compared between groups. In addition, the associations between abnormal brain regions and clinical features were evaluated by Pearson correlation analyses. Compared to HCs, pED patients demonstrated decreased fALFF values in the left medial superior frontal gyrus (had decreased FC values with the left dorsolateral superior frontal gyrus), the left lingual gyrus (had decreased FC values with the left parahippocamal gyrus and insula), the left putamen (had decreased FC values with the right caudate) and the right putamen (had decreased FC values with the left putamen and the right caudate). The fALFF values of the left medial superior frontal gyrus were negatively correlated with the fifth item scores of International Index of Erectile Function (IIEF-5). Negative relationships were found between fALFF values of the left putamen and the second item scores of Arizona Sexual Scale (ASEX). FC values between the right putamen and caudate were negatively related to the state scores of State-Trait Anxiety Inventory (STAI-S). Altered brain function were found in the medial superior frontal gyrus and caudate-putamen of pED patients, which were associated with sexual function and psychological condition. These findings provided new insights into the central pathological mechanisms of pED.

Abnormal changes of static and dynamic functional connectivity of dopaminergic midbrain in patients with first-episode schizophrenia and their correlations with clinical symptoms

Objective: To investigate the abnormal changes of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in the dopaminergic midbrain (ventral dorsal tegmental area and bilateral substantia nigra compacta, VTA/SNc) in patients with first-episode schizophrenia(SCH), and their correlation with the Positive and Negative Symptom Scale (PANSS). Methods: The data of 198 first-episode untreated schizophrenia patients and 199 healthy controls (HC) matched by age, sex and years of education who were admitted to the First Affiliated Hospital of Zhengzhou University from January 2019 to May 2022 were prospectively collected. All subjects underwent high resolution structural MRI and resting state functional magnetic resonance imaging (rs-fMRI) scanning. The dopaminergic midbrain (VTA/SNc) was defined as three regions of interest (ROI). The sFC and dFC analyses with VTA/SNc as seeds were performed to produce a whole-brain diagram initially, which subsequently were compared between schizophrenia group and HC group. Finally, the correlation analysis of sFC and dFC values with the PANSS scores were performed, including the positive scale score, negative scale score, general psychopathology scale score, total score and symptom scores. Results: There were 86 males and 112 females in SCH group, and aged (23±9) years. Meanwhile, there were 95 males and 104 females in HC group, and aged (22±5) years. In the SCH group, the positive (P), the negative (N) and the general psychopathology (G) scale scores and the total score (T) of the PANSS scale was 20±7, 21±7, 41±11 and 82±22, respectively. Compared with the HC group, the VTA showed decreased sFC with four clusters including cerebellar vermis 7/9, left putamen, right thalamus and left middle cingulate gyrus in the schizophrenia group (peak center, t=-4.35, -4.81, -4.35 and -4.65; voxel P<0.005; cluster P<0.05), the right SNc showed decreased sFC with four clusters including left cerebellar hemisphere 4/5/8, right putamen, right medial orbitofrontal gyrus and the left putamen in the schizophrenia group (peak center, t=-4.91, -5.15, -4.77 and -5.21; voxel P<0.005; cluster P<0.05), and the left SNc showed decreased sFC with four clusters including the left putamen, right putamen, right medial orbitofrontal gyrus and left middle cingulate gyrus in the schizophrenia group (peak center, t=-5.82, -4.83 and -4.65; voxel P<0.005; cluster P<0.05). Compared with the HC group, the VTA showed decreased dFC with the right inferior parietal gyrus, right angular gyrus and right superior parietal gyrus in schizophrenia group (t=-4.17). In the schizophrenia group, the sFC value of cluster 2 (left putamen) with VTA as seed and cluster 4 (left putamen) with right SNc as seed were positively correlated with the positive scale scores in PANSS (r=0.141, 0.169, both P<0.05). The sFC and dFC values of significant regions were also correlated with hallucination, delusion, suspicion, hostility, communication disorder, passivity/indifference, lack of communication, stereotyped thinking, depression, non-cooperation, lack of judgment and insight, impulse control disorder, active social avoidance (all P<0.05). Conclusion: The static and dynamic functional connectivity (stability) of VTA/SNc to cerebellum, thalamus, striatum, prefrontal lobe and cingulate gyrus in first-episode schizophrenia patients were decreased, which were closely related to the positive and negative symptoms of schizophrenia.

Neurocognitive function and quality-of-life in patients with colorectal cancer.

Colorectal cancer (CRC) survivors report significant long-term physical and cognitive declines post-treatment. Our purpose was to combine task-evoked Event-Related Potential (ERP) and resting state functional magnetic resonance imaging (rsf/MRI) methodologies to characterize the physiological underpinnings and cognitive sequelae of chemotherapy-related cognitive impairment, including changes in Quality-Of-Life (QOL) in patients with CRC, as compared to healthy control (HC) participants. This descriptive study recruited and obtained baseline data from patients with CRC at medical and surgical oncology visits four to six weeks post-op and followed them at 12- and 24-weeks. Procedures employed ERP, pencil and paper neuropsychological testing (N-P), structural/functional rsf/MRI, and self-report QOL methodologies. Data analyses included correlations, one-way ANOVA, Chi-square tests, and linear mixed models. Study participants (n=40) across groups (n=15, 11, 14) were balanced on age, sex, education, and race, but not marital status Several significant associations were found between changes in Dorsal Attention Network (DAN)-related ERP measures (P2, N2, N2P2, N2pc amplitudes), with QOL measures between baseline and last visits (p<0.05-0.001). Additionally, rsf/MRI findings showed increased network activity in a single node of the DAN post-treatment, which was associated with poorer performance on N-P tests of attention and working memory, as well as a focal decline in grey matter volume in the area. Our methodology revealed structural and functional changes within the DAN associated with altered spatial attention, working memory, and ability to inhibit. These disruptions may be responsible for decreased QOL ratings in patients with CRC. This study provides a putative mechanism of understanding how altered brain structural/functional relationships impact cognition, QOL, and nursing care in patients with CRC. NCI-2020-05952, University of Nebraska Medical Center, Clinical Trials.gov ID NCT03683004.

Conectividad funcional en un paciente con alucinaciones complejas. Un caso de autoscopia

La autoscopia, del griego autos (sí mismo) y skopeo (mirar), son alucinaciones complejas durante las cuales el paciente experimenta una duplicación visual de su cuerpo. El presente artículo discute algunos correlatos de conectividad cerebral obtenida por resonancia funcional en reposo, en una paciente con epilepsia refractaria y alucinaciones autoscópicas durante sus crisis.&#x0D; El objetivo es presentar los hallazgos por neuroimágenes funcionales de una paciente que presenta alucinaciones del tipo autoscópico.&#x0D; Como parte del protocolo de evaluación de pacientes con epilepsia refractaria se realiza examen de videomonitoreo electroencefalográfico, imágenes de tomografía por emisión de positrones e imágenes de resonancia magnética funcional en reposo a una paciente que sufre alucinaciones autoscópicas durante las crisis. La información obtenida por estos estudios se correlaciona con la información clínica.&#x0D; En la paciente, el estudio de tomografía por emisión de positrones y el videomonitoreo electroencefalográfico indican alteraciones en el lóbulo occipital derecho, hallazgos que coinciden con reportes en la literatura sobre autoscopia. La sincronización neuronal, medida por resonancia funcional, es anormal en la misma región alterada en la tomografía por emisión de positrones, permitiendo relacionar el fenómeno autoscópico con cambios de la conectividad cerebral. La paciente cuenta con características clínicas de alucinación autoscópica. La resonancia magnética indica zonas con alteraciones de la dinámica cerebral que, en el caso de la paciente, coinciden con regiones reportadas en la literatura. El presente estudio aporta evidencias sobre el papel de los mecanismos de sincronización neuronal y la percepción de alucinaciones.

Dynamic functional brain networks in Alzheimer’s disease and healthy ageing

AbstractBackgroundBrain network analysis from resting‐state functional MRI (fMRI) has helped us to understand cortical activity in dementia. Likewise, dynamic brain networks' analysis, which takes into account temporal fluctuations in the resting‐state fMRI signal, has helped to reveal patterns of activity, usually averaged out by the conventional functional network analysis. These patterns of activity, or dynamic networks, reveal transient (meta‐stable) dynamics. Dynamic functional networks analysis from fMRI data has shown a potential to unveil clinically relevant information.MethodResting‐state fMRI from 187 participants belonging to Alzheimer’s Disease (AD) and cognitively‐normal Healthy Elderly (HE) from the ADNI‐3 database were analysed. Functional network was constructed from fMRI signal of the 120 cortical and sub‐cortical regions of the Jülich Atlas for each participants, and averaged across the two study groups. Dynamic functional connectivity is calculated using sliding window approach and compared to conventional (‘static’) functional connectivity within each study group and between them.ResultSignificant differences when comparing AD with HE individuals were found in the dynamic, but not static functional networks. These differences were restricted to the white matter regions, and to the inferior and superior parietal, and somatosensory cortices.ConclusionOur results demonstrate the existence of common and distinct patterns of static and dynamic brain connectivity in HE and AD, which highlight the importance of including non‐stationary information into the brain network analysis in Alzheimer’s disease.

Default mode and frontoparietal network dynamics: Associations with familial risk for depression and stress sensitivity

BackgroundMajor Depressive Disorder (MDD) is associated with alterations within the default mode (DMN) and frontoparietal (FPN) networks. However, it is unclear whether changes in these networks occur prior to onset in youth at high familial risk for MDD or are a consequence of MDD. Moreover, studies examining premorbid MDD vulnerability markers have focused on static rather than dynamic network properties, which could further elucidate DMN-FPN imbalances linked to MDD risk. MethodsEighty-nine unaffected 12–14-year-old adolescents both with (n = 27) and without (n = 62) a maternal history of MDD completed a resting state functional magnetic resonance imaging scan and self-report assessments of depressive symptoms and perceived stress at baseline and every three months across a two-year span. A coactivation pattern (CAP) analysis was conducted to examine functional network dynamic properties, including time spent in each CAP (total number of volumes), CAP persistence (number of consecutive volumes in each CAP), and number of transitions between posterior DMN-FPN and canonical DMN CAPs. Multilevel models estimated whether DMN-FPN dynamic properties predicted future depressive symptoms and stress sensitivity. ResultsHigh-risk adolescents spent more time and exhibited a longer persistence in a posterior DMN-FPN CAP. DMN-FPN CAP persistence predicted future perceived stress, but only among high-risk adolescents. High-risk adolescents characterized by high DMN-FPN persistence reported greater future perceived stress, whereas those showing low DMN-FPN persistence had reduced perceived stress over time. Unexpectedly, DMN-FPN dynamics did not predict future depressive symptoms. ConclusionsAltered DMN-FPN CAP properties among high-risk adolescents mirror alterations among individuals with MDD, suggesting that DMN-FPN dynamics may be a risk marker rather than consequence of MDD. Furthermore, longer DMN-FPN CAP persistence increases vulnerability in high-risk adolescents by predicting greater future stress sensitivity, a well-known catalyst for MDD. Replication in a larger sample is warranted.

Altered Functional Connectivity of the Nucleus Accumbens and Amygdala in Cyber Addiction: A Resting State Functional Magnetic Resonance Imaging Study

Cyber addiction, which is more vulnerable in adolescents, is defined as the excessive use of computers and the Internet that causes serious psychological, social, and physical problems. In this study, we investigated the resting-state functional connectivity (rsFC) in adolescents with cyber addiction. We collected and analyzed resting-state functional neuroimaging data of 20 patients with cyber addiction, aged 13-18 years, and 27 healthy controls. Based on previous studies, the seed regions included the dorsolateral prefrontal cortex, medial orbitofrontal cortex, lateral orbitofrontal cortex, dorsal anterior cingulate cortex, insula, hippocampus, amygdala, nucleus accumbens, and the ventral tegmental area. Seed-to-voxel analyses were performed to investigate the differences between patients and healthy controls. A correlation analysis between rsFC and cyber addiction severity was also performed. Patients with cyber addiction showed the following characteristics: increased positive rsFC between the left insular-right middle temporal gyrus; increased positive rsFC between the right hippocampus-right precentral gyrus; increased positive rsFC between the right amygdala-right precentral gyrus and right parietal operculum cortex; increased negative rsFC between the left nucleus accumbens-right cerebellum crus II and right cerebellum VI. Adolescents with cyber addiction show altered functional connectivity during the resting state. The findings of this study may help us better understand the neuropathology of cyber addiction in adolescents.

Salience and hedonic experience as predictors of central stimulant treatment response in ADHD – A resting state fMRI study

BackgroundRoughly 20–30% of patients with Attention-deficit/hyperactivity disorder (ADHD) fail to respond to central stimulant (CS) medication. Genetic, neuroimaging, biochemical and behavioral biomarkers for CS response have been investigated, but currently there are no biomarkers available for clinical use that help identify CS responders and non-responders. MethodsIn the present paper, we studied if incentive salience and hedonic experience evaluated after a single-dose CS medication could predict response and non-response to CS medication. We used a bipolar visual analogue ‘wanting’ and ‘liking’ scale to gauge incentive salience and hedonic experience in 25 healthy controls (HC) and 29 ADHD patients. HC received 30 mg methylphenidate (MPH) and ADHD patients received either MPH or lisdexamphetamine (LDX) as selected by their clinician, with dosage individually determined for optimal effect. Clinician-evaluated global impression – severity (CGI-S) and improvement (CGI-I) and patient-evaluated improvement (PGI-I) were used to assess response to CS medication. Resting state functional magnetic resonance imaging (fMRI) was conducted before and after single-dose CS to correlate wanting and liking scores to changes in functional connectivity. ResultsRoughly 20% of the ADHD patients were CS non-responders (5 of 29). CS responders had significantly higher incentive salience and hedonic experience scores compared to healthy controls and CS non-responders. Resting state fMRI showed that wanting scores were significantly associated to changes in functional connectivity in ventral striatum including nucleus accumbens. ConclusionIncentive salience and hedonic experience evaluated after a single-dose CS medication segregate CS responders and non-responders, with corresponding neuroimaging biomarkers in the brain reward system.

The impact of wearing a KN95 face mask on human brain function: evidence from resting state functional magnetic resonance imaging.

Face masks are widely used in daily life because of the COVID-19 pandemic. The objective of this study was to explore the impact of wearing face masks on brain functions by using resting-state functional MRI (RS-fMRI). Scanning data from 15 healthy subjects (46.20 ± 6.67 years) were collected in this study. Each subject underwent RS-fMRI scans under two comparative conditions, wearing a KN95 mask and natural breathing (no mask). The amplitude of low frequency fluctuation (ALFF) and functional connectivity under the two conditions were analyzed and then compared using the paired t-test. Compared with those of the no-mask condition, the ALFF activities when wearing masks were increased significantly in the right middle frontal gyrus, bilateral precuneus, right superior marginal gyrus, left inferior parietal gyrus, and left supplementary motor area and decreased significantly in the anterior cingulate gyrus, right fusiform gyrus, left superior temporal gyrus, bilateral lingual gyrus, and bilateral calcarine cortex (p < 0.05). Taking the posterior cingulate cortex area as a seed point, the correlations with the occipital cortex, prefrontal lobe, and motor sensory cortex were sensitive to wearing masks compared with not wearing masks (p < 0.05). Taking the medial prefrontal cortex region as a seed point, the functional connectivity with the bilateral temporal lobe, bilateral motor sensory cortex, and occipital lobe was influenced by wearing a KN95 mask (p < 0.05). This study demonstrated that wearing a KN95 face mask can cause short-term changes in human resting brain function. Both local neural activities and functional connectivity in brain regions were sensitive to mask wearing. However, the neural mechanism causing these changes and its impact on cognitive function still need further investigation.

Overnight Abstinence, Ventrostriatal-Insular Connectivity, and Tridimensional Personality Traits in Cigarette Smokers.

Personality traits contribute to the risks of smoking. The striatum has been implicated in nicotine addiction and nicotine deprivation is associated with alterations in resting state functional connectivity (rsFC) of the ventral (VS) and dorsal (DS) striatum. However, it remains unclear how striatal rsFC may change following overnight abstinence or how these shorter-term changes in inter-regional connectivity relate to personality traits. In the current study, 28 smokers completed assessments with Fagerström Test of Nicotine Dependence, Tridimensional Personality Questionnaire (TPQ), as well as resting state functional magnetic resonance imaging (fMRI) scans during satiety and after overnight abstinence. We processed imaging data with published routines and evaluated the results with a corrected threshold. Smokers showed increases in the VS-insula rsFC but no significant changes in the DS rsFC after overnight abstinence as compared to satiety. The difference in the VS-insula rsFC (abstinence minus satiety) was negatively correlated with harm avoidance. These findings highlighted striatal connectivity correlates of very short-term abstinence from smoking and how the VS-insula rsFC may vary with individual personality traits, interlinking neural markers and personality risk factors of cigarette smoking at the earliest stage of abstinence.

Hodge Laplacian of Brain Networks.

The closed loops or cycles in a brain network embeds higher order signal transmission paths, which provide fundamental insights into the functioning of the brain. In this work, we propose an efficient algorithm for systematic identification and modeling of cycles using persistent homology and the Hodge Laplacian. Various statistical inference procedures on cycles are developed. We validate the our methods on simulations and apply to brain networks obtained through the resting state functional magnetic resonance imaging. The computer codes for the Hodge Laplacian are given in https://github.com/laplcebeltrami/hodge.

Fixed Time-Point Analysis Reveals Repetitive Mild Traumatic Brain Injury Effects on Resting State Functional Magnetic Resonance Imaging Connectivity and Neuro-Spatial Protein Profiles.

Repetitive mild traumatic brain injuries (rmTBIs) are serious trauma events responsible for the development of numerous neurodegenerative disorders. A major challenge in developing diagnostics and treatments for the consequences of rmTBI is the fundamental knowledge gaps of the molecular mechanisms responsible for neurodegeneration. It is both critical and urgent to understand the neuropathological and functional consequences of rmTBI to develop effective therapeutic strategies. Using the Closed-Head Impact Model of Engineered Rotational Acceleration, or CHIMERA, we measured neural changes following injury, including brain volume, diffusion tensor imaging, and resting-state functional magnetic resonance imaging coupled with graph theory and functional connectivity analyses. We determined the effect of rmTBI on markers of gliosis and used NanoString-GeoMx to add a digital-spatial protein profiling analysis of neurodegenerative disease-associated proteins in gray and white matter regions. Our analyses revealed aberrant connectivity changes in the thalamus, independent of microstructural damage or neuroinflammation. We also identified distinct changes in the levels of proteins linked to various neurodegenerative processes including total and phospho-tau species and cell proliferation markers. Together, our data show that rmTBI significantly alters brain functional connectivity and causes distinct protein changes in morphologically intact brain areas.