Most recent paper

Effect of dopamine on limbic network connectivity at rest in Parkinson's disease patients with freezing of gait

Mon, 05/06/2024 - 18:00

Transl Neurosci. 2024 May 2;15(1):20220336. doi: 10.1515/tnsci-2022-0336. eCollection 2024 Jan 1.


BACKGROUND: Freezing of gait (FOG) in Parkinson's disease (PD) has a poorly understood pathophysiology, which hinders treatment development. Recent work showed a dysfunctional fronto-striato-limbic circuitry at rest in PD freezers compared to non-freezers in the dopamine "OFF" state. While other studies found that dopaminergic replacement therapy alters functional brain organization in PD, the specific effect of dopamine medication on fronto-striato-limbic functional connectivity in freezers remains unclear.

OBJECTIVE: To evaluate how dopamine therapy alters resting state functional connectivity (rsFC) of the fronto-striato-limbic circuitry in PD freezers, and whether the degree of connectivity change is related to freezing severity and anxiety.

METHODS: Twenty-three PD FOG patients underwent MRI at rest (rsfMRI) in their clinically defined "OFF" and "ON" dopaminergic medication states. A seed-to-seed based analysis was performed between a priori defined limbic circuitry ROIs. Functional connectivity was compared between OFF and ON states. A secondary correlation analyses evaluated the relationship between Hospital Anxiety and Depression Scale (HADS)-Anxiety) and FOG Questionnaire with changes in rsFC from OFF to ON.

RESULTS: PD freezers' OFF compared to ON showed increased functional coupling between the right hippocampus and right caudate nucleus, and between the left putamen and left posterior parietal cortex (PPC). A negative association was found between HADS-Anxiety and the rsFC change from OFF to ON between the left amygdala and left prefrontal cortex, and left putamen and left PPC.

CONCLUSION: These findings suggest that dopaminergic medication partially modulates the frontoparietal-limbic-striatal circuitry in PD freezers, and that the influence of medication on the amygdala, may be related to clinical anxiety in freezer.

PMID:38708096 | PMC:PMC11066616 | DOI:10.1515/tnsci-2022-0336

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping

Mon, 05/06/2024 - 18:00

Brain Commun. 2024 May 4;6(3):fcae094. doi: 10.1093/braincomms/fcae094. eCollection 2024.


Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

PMID:38707706 | PMC:PMC11069118 | DOI:10.1093/braincomms/fcae094

Association between homotopic connectivity and clinical symptoms in first-episode schizophrenia

Mon, 05/06/2024 - 18:00

Heliyon. 2024 Apr 25;10(9):e30347. doi: 10.1016/j.heliyon.2024.e30347. eCollection 2024 May 15.


BACKGROUND: Abnormal functional connectivity (FC) in the brain has been observed in schizophrenia patients. However, studies on FC between homotopic brain regions are limited, and the results of these studies are inconsistent. The aim of this study was to compare homotopic connectivity between first-episode schizophrenia (FES) patients and healthy subjects and assess its correlation with clinical symptoms.

METHODS: Thirty-one FES patients and thirty-three healthy controls (HC) were included in the study. The voxel-mirrored homotopic connectivity (VMHC) method of resting-state functional magnetic resonance imaging (rs-fMRI) was used to analyse the changes in homotopic connectivity between the two groups. The 5-factor PANSS model was used to quantitatively evaluate the severity of symptoms in FES patients. Partial correlation analysis was used to assess the correlation between homotopic connectivity changes and clinical symptoms.

RESULTS: Compared to those in the HC group, VMHC values were decreased in the paracentral lobule (PL), thalamus, and superior temporal gyrus (STG) in the FES group (P < 0.05, FDR correction). No significant differences in white matter volume (WMV) within the subregion of the corpus callosum or in brain regions associated with reduced VMHC were observed between the two groups. Partial correlation analyses revealed that VMHC in the bilateral STG of FES patients was positively correlated with negative symptoms (rleft = 0.46, p < 0.05; rright = 0.47, p < 0.05), and VMHC in the right thalamus was negatively correlated with disorganized/concrete symptoms (rright = 0.45, p < 0.05).

CONCLUSION: Our study revealed that homotopic connectivity is altered in the resting-state brain of FES patients and correlates with the severity of negative symptoms; this change may be independent of structural changes in white matter. These findings may contribute to the development of the abnormal connectivity hypothesis in schizophrenia patients.

PMID:38707391 | PMC:PMC11066690 | DOI:10.1016/j.heliyon.2024.e30347

Macronutrient intake is associated with intelligence and neural development in adolescents

Mon, 05/06/2024 - 18:00

Front Nutr. 2024 Apr 18;11:1349738. doi: 10.3389/fnut.2024.1349738. eCollection 2024.


INTRODUCTION: Macronutrient intake can be one of the most influential factors in cognitive and neural development in adolescents. Adolescence is a specific period of cognitive and neural development, and nutritional effects during this period could be life-long. Therefore, understanding the effects of macronutrient intake on cognitive and neural development in adolescents is crucially important. We thus examined the association across macronutrient intake, intelligence, and neural development using population-based cohort data.

METHODS: We conducted two studies. In study 1, we included a total of 1,734 participants (boys, 907, age [mean ± standard deviation] 171.9 ± 3.44 months; range 163.0-186.0 months) from the Tokyo TEEN Cohort (TTC) to examine the association between macronutrient intake and intelligence quotient (IQ). In study 2, we included a total of 63 participants (boys, 38, age 174.4 ± 7.7 months; range 160.7-191.6 months) to investigate the effect of nutrition intake on neural development using graph theory analysis for resting-state functional magnetic resonance imaging (rs-fMRI) derived from a subset of the TTC.

RESULTS: TTC data revealed that a higher IQ was associated in boys with increased protein intake (β = 0.068, p = 0.031), and in girls, with reduced carbohydrate intake (β = -0.076, p = 0.024). Graph theory analysis for rs-fMRI at approximately age 12 has shown that impaired local efficiency in the left inferior frontal gyrus was associated with higher carbohydrate and fat intake ([x, y, z] = [-51, 23, 8], pFDR-corrected = 0.00018 and 0.02290, respectively), whereas increased betweenness centrality in the left middle temporal gyrus was associated with higher carbohydrate, fat, and protein intake ([x, y, z] = [-61, -43, -13], pFDR-corrected = 0.0027, 0.0029, and 0.00075, respectively). Moreover, we identified a significant moderating effect of fat and protein intake on the relationship between change in betweenness centrality over a 2-year measurement gap in the left middle temporal gyrus and intelligence (β = 12.41, p = 0.0457; β = 12.12, p = 0.0401, respectively).

CONCLUSION: Our study showed the association between macronutrient intake and neural development related to intelligence in early adolescents. Appropriate nutritional intake would be a key factor for healthy cognitive and neural development.

PMID:38706562 | PMC:PMC11067507 | DOI:10.3389/fnut.2024.1349738

Potential correlations between asymmetric disruption of functional connectivity and metabolism in major depressive disorder

Sun, 05/05/2024 - 18:00

Brain Res. 2024 May 3:148977. doi: 10.1016/j.brainres.2024.148977. Online ahead of print.


OBJECTIVE: Previous research has suggested a connection between major depressive disorder (MDD) and certain comorbidities, including gastrointestinal issues, thyroid dysfunctions, and glycolipid metabolism abnormalities. However, the relationships between these factors and asymmetrical alterations in functional connectivity (FC) in adults with MDD remain unclear.

METHOD: We conducted a study on a cohort of 42 MDD patients and 42 healthy controls (HCs). Participants underwent comprehensive clinical assessments, including evaluations of blood lipids and thyroid hormone levels, as well as resting-state functional magnetic resonance imaging (Rs-fMRI) scans. Data analysis involved correlation analysis to compute the parameter of asymmetry (PAS) for the entire brain's functional connectome. We then examined the interrelationships between abnormal PAS regions in the brain, thyroid hormone levels, and blood lipid levels.

RESULTS: The third-generation ultra-sensitive thyroid stimulating hormone (TSH3UL) level was found to be significantly lower in MDD patients compared to HCs. The PAS score of the left inferior frontal gyrus (IFG) decreased, while the bilateral posterior cingulate cortex (Bi-PCC) PAS increased in MDD patients relative to HCs. Notably, the PAS score of the left IFG negatively correlated with both TSH and total cholesterol (CHOL) levels. However, these correlations lose significance after the Bonferroni correction.

CONCLUSION: MDD patients demonstrated abnormal asymmetry in resting-state FC (Rs-FC) within the fronto-limbic system, which may be associated with CHOL and thyroid hormone levels.

PMID:38705556 | DOI:10.1016/j.brainres.2024.148977

Change in resting state functional connectivity following working memory training in individuals with repetitive negative thinking

Sun, 05/05/2024 - 18:00

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 May 3:S2451-9022(24)00119-8. doi: 10.1016/j.bpsc.2024.04.017. Online ahead of print.


BACKGROUND: Repetitive negative thinking (RNT) symptoms, which are characterized by pervasive, uncontrollable negative thoughts, are common in individuals with mood, anxiety, and traumatic stress disorders. Inability to regulate the contents of working memory is a hypothesized etiological factor in RNT, suggesting that training to improve working memory may be beneficial. This study examined the effects of working memory training on resting state functional connectivity (rsFC) in individuals with elevated RNT and whether such changes would be associated with clinical improvement.

METHODS: We conducted a secondary analysis of pre-post resting state data collected as part of a randomized controlled trial [NCT04912089] of working memory training interventions (n=42) compared to a waitlist control group (n=23). We hypothesized that individuals completing training would show increased rsFC between the two key intrinsic connectivity networks - default mode network (posterior cingulate cortex; PCC) and frontoparietal network (dorsolateral prefrontal cortex; dlPFC). We explored whether magnitude of rsFC change was associated with change in RNT symptom severity.

RESULTS: rsFC increased between the PCC and regions including frontal and parietal cortex in the training group relative to waitlist. Increased connectivity between the PCC and superior frontal cortex was associated with RNT symptom reduction.

CONCLUSIONS: These data provide evidence that working memory training can modulate neural circuitry at rest in individuals with RNT. Results align with accounts of working memory training effects on large-scale neurocircuitry changes and suggest that these changes may contribute to clinical promise of this type of intervention on transdiagnostic RNT symptoms.

PMID:38705463 | DOI:10.1016/j.bpsc.2024.04.017

Resting-state fMRI network efficiency as a mediator in the relationship between the glymphatic system and cognitive function in obstructive sleep apnea hypopnea syndrome: Insights from a DTI-ALPS investigation

Sun, 05/05/2024 - 18:00

Sleep Med. 2024 May 3;119:250-257. doi: 10.1016/j.sleep.2024.05.009. Online ahead of print.


INTRODUCTION: Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with cognitive impairment and physiological complications, necessitating further understanding of its mechanisms. This study investigates the relationship between glymphatic system function, brain network efficiency, and cognitive impairment in OSAHS patients using diffusion tensor image analysis along the perivascular space (DTI-ALPS) and resting-state fMRI.

MATERIALS AND METHODS: This study included 31 OSAHS patients and 34 age- and gender-matched healthy controls (HC). All participants underwent GE 3.0T magnetic resonance imaging (MRI) with diffusion tensor image (DTI) and resting-state fMRI scans. The DTI-ALPS index and brain functional networks were assessed. Differences between groups and correlations with clinical characteristics were analyzed. Additionally, the mediating role of brain network efficiency was explored. Finally, receiver operating characteristics (ROC) analysis assessed diagnostic performance.

RESULTS: OSAHS patients had significantly lower ALPS-index (1.268 vs. 1.431, p < 0.0001) and moderate negative correlation with Apnea Hypopnea Index (AHI) (r = -0.389, p = 0.031), as well as moderate positive correlation with Montreal Cognitive Assessment (MoCA) (r = 0.525, p = 0.002). Moreover, global efficiency (Eg) of the brain network was positively correlated with the ALPS-index and MoCA scores in OSAHS patients (r = 0.405, p = 0.024; r = 0.56, p = 0.001, respectively). Furthermore, mediation analysis showed that global efficiency partially mediated the impact of glymphatic system dysfunction on cognitive impairment in OSAHS patients (indirect effect = 4.58, mediation effect = 26.9 %). The AUROC for identifying OSAHS and HC was 0.80 (95 % CI 0.69 to 0.91) using an ALPS-index cut-off of 1.35.

CONCLUSIONS: OSAHS patients exhibit decreased ALPS-index, indicating impaired glymphatic system function. Dysfunction of the glymphatic system can affect cognitive function in OSAHS by disrupting brain functional network, suggesting a potential underlying pathological mechanism. Additionally, preliminary findings suggest that the ALPS-index may offer promise as a potential indicator for OSAHS.

PMID:38704873 | DOI:10.1016/j.sleep.2024.05.009

Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy

Sat, 05/04/2024 - 18:00

Neurol Sci. 2024 May 4. doi: 10.1007/s10072-024-07506-8. Online ahead of print.


BACKGROUND: Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels.

RESULTS: Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN.

CONCLUSIONS: Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

PMID:38704479 | DOI:10.1007/s10072-024-07506-8

Fractional amplitude of low-frequency fluctuations in sensory-motor networks and limbic system as a potential predictor of treatment response in patients with schizophrenia

Sat, 05/04/2024 - 18:00

Schizophr Res. 2024 May 3:S0920-9964(24)00173-7. doi: 10.1016/j.schres.2024.04.020. Online ahead of print.


BACKGROUND: Previous investigations have revealed substantial differences in neuroimaging characteristics between healthy controls (HCs) and individuals diagnosed with schizophrenia (SCZ). However, we are not entirely sure how brain activity links to symptoms in schizophrenia, and there is a need for reliable brain imaging markers for treatment prediction.

METHODS: In this longitudinal study, we examined 56 individuals diagnosed with 56 SCZ and 51 HCs. The SCZ patients underwent a three-month course of antipsychotic treatment. We employed resting-state functional magnetic resonance imaging (fMRI) along with fractional Amplitude of Low Frequency Fluctuations (fALFF) and support vector regression (SVR) methods for data acquisition and subsequent analysis.

RESULTS: In this study, we initially noted lower fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, coupled with higher fALFF values in the left hippocampus and right putamen in SCZ patients compared to the HCs at baseline. However, when comparing fALFF values in brain regions with abnormal baseline fALFF values for SCZ patients who completed the follow-up, no significant differences in fALFF values were observed after 3 months of treatment compared to baseline data. The fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, and the left postcentral gyrus were useful in predicting treatment effects.

CONCLUSION: Our findings suggest that reduced fALFF values in the sensory-motor networks and increased fALFF values in the limbic system may constitute distinctive neurobiological features in SCZ patients. These findings may serve as potential neuroimaging markers for the prognosis of SCZ patients.

PMID:38704344 | DOI:10.1016/j.schres.2024.04.020

Verbal memory network mapping in individual patients predicts postoperative functional impairments

Sat, 05/04/2024 - 18:00

Hum Brain Mapp. 2024 May;45(7):e26691. doi: 10.1002/hbm.26691.


Verbal memory decline is a significant concern following temporal lobe surgeries in patients with epilepsy, emphasizing the need for precision presurgical verbal memory mapping to optimize functional outcomes. However, the inter-individual variability in functional networks and brain function-structural dissociations pose challenges when relying solely on group-level atlases or anatomical landmarks for surgical guidance. Here, we aimed to develop and validate a personalized functional mapping technique for verbal memory using precision resting-state functional MRI (rs-fMRI) and neurosurgery. A total of 38 patients with refractory epilepsy scheduled for surgical interventions were enrolled and 28 patients were analyzed in the study. Baseline 30-min rs-fMRI scanning, verbal memory and language assessments were collected for each patient before surgery. Personalized verbal memory networks (PVMN) were delineated based on preoperative rs-fMRI data for each patient. The accuracy of PVMN was assessed by comparing post-operative functional impairments and the overlapping extent between PVMN and surgical lesions. A total of 14 out of 28 patients experienced clinically meaningful declines in verbal memory after surgery. The personalized network and the group-level atlas exhibited 100% and 75.0% accuracy in predicting postoperative verbal memory declines, respectively. Moreover, six patients with extra-temporal lesions that overlapped with PVMN showed selective impairments in verbal memory. Furthermore, the lesioned ratio of the personalized network rather than the group-level atlas was significantly correlated with postoperative declines in verbal memory (personalized networks: r = -0.39, p = .038; group-level atlas: r = -0.19, p = .332). In conclusion, our personalized functional mapping technique, using precision rs-fMRI, offers valuable insights into individual variability in the verbal memory network and holds promise in precision verbal memory network mapping in individuals.

PMID:38703114 | DOI:10.1002/hbm.26691

Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Sat, 05/04/2024 - 18:00

Neuroimage. 2024 May 2:120633. doi: 10.1016/j.neuroimage.2024.120633. Online ahead of print.


Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso-scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso-scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso-scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.

PMID:38704057 | DOI:10.1016/j.neuroimage.2024.120633

Oscillation-specific nodal differences in Parkinson's disease patients with anxiety

Fri, 05/03/2024 - 18:00

J Parkinsons Dis. 2024 Apr 29. doi: 10.3233/JPD-240055. Online ahead of print.


BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder that is predominantly known for its motor symptoms but is also accompanied by non-motor symptoms, including anxiety.

OBJECTIVE: The underlying neurobiological substrates and brain network changes associated with comorbid anxiety in PD require further exploration.

METHODS: An analysis of oscillation-specific nodal properties in patients with and without anxiety was conducted using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory. We used a band-pass filtering approach to differentiate oscillatory frequency bands for subsequent functional connectivity (FC) and graph analyses.

RESULTS: The study included 68 non-anxiety PD (naPD) patients, 62 anxiety PD (aPD) patients, and 64 healthy controls (NC). Analyses of nodal betweenness centrality (BC), degree centrality (DC), and efficiency were conducted across multiple frequency bands. The findings indicated no significant differences in BC among naPD, aPD, and NC within the 0.01-0.08 Hz frequency range. However, we observed a specific reduction in BC at narrower frequency ranges in aPD patients, as well as differing patterns of change in DC and efficiency, which are believed to reflect the neurophysiological bases of anxiety symptoms in PD.

CONCLUSIONS: Differential oscillation-specific nodal characteristics have been identified in PD patients with anxiety, suggesting potential dysregulations in brain network dynamics. These findings emphasize the complexity of brain network alterations in anxiety-associated PD and identify oscillatory frequencies as potential biomarkers. The study highlights the importance of considering oscillatory frequency bands in the analysis of brain network changes.

PMID:38701162 | DOI:10.3233/JPD-240055

Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation

Fri, 05/03/2024 - 18:00

PLoS Comput Biol. 2024 May 3;20(5):e1011350. doi: 10.1371/journal.pcbi.1011350. eCollection 2024 May.


A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was defined by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness.

PMID:38701063 | DOI:10.1371/journal.pcbi.1011350

A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis

Fri, 05/03/2024 - 18:00

IEEE J Biomed Health Inform. 2024 May 3;PP. doi: 10.1109/JBHI.2024.3396457. Online ahead of print.


Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and individualized information of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

PMID:38700974 | DOI:10.1109/JBHI.2024.3396457

Nomogram for prediction of hearing rehabilitation outcome in children with congenital sensorineural hearing loss after cochlear implantation

Fri, 05/03/2024 - 18:00

Heliyon. 2024 Apr 16;10(8):e29529. doi: 10.1016/j.heliyon.2024.e29529. eCollection 2024 Apr 30.


BACKGROUND: Reliable predictors for rehabilitation outcomes in patients with congenital sensorineural hearing loss (CSNHL) after cochlear implantation (CI) are lacking. The purchase of this study was to develop a nomogram based on clinical characteristics and neuroimaging features to predict the outcome in children with CSNHL after CI.

METHODS: Children with CSNHL prior to CI surgery and children with normal hearing were enrolled into the study. Clinical data, high resolution computed tomography (HRCT) for ototemporal bone, conventional brain MRI for structural analysis and brain resting-state fMRI (rs-fMRI) for the power spectrum assessment were assessed. A nomogram combining both clinical and imaging data was constructed using multivariate logistic regression analysis. Model performance was evaluated and validated using bootstrap resampling.

RESULTS: The final cohort consisted of 72 children with CSNHL (41 children with poor outcome and 31 children with good outcome) and 32 healthy controls. The white matter lesion from structural assessment and six power spectrum parameters from rs-fMRI, including Power4, Power13, Power14, Power19, Power23 and Power25 were used to build the nomogram. The area under the receiver operating characteristic (ROC) curve of the nomogram obtained using the bootstrapping method was 0.812 (95 % CI = 0.772-0.836). The calibration curve showed no statistical difference between the predicted value and the actual value, indicating a robust performance of the nomogram. The clinical decision analysis curve showed a high clinical value of this model.

CONCLUSIONS: The nomogram constructed with clinical data, and neuroimaging features encompassing ototemporal bone measurements, white matter lesion values from structural brain MRI and power spectrum data from rs-fMRI showed a robust performance in predicting outcome of hearing rehabilitation in children with CSNHL after CI.

PMID:38699755 | PMC:PMC11063407 | DOI:10.1016/j.heliyon.2024.e29529

The co-activation patterns of multiple brain regions in Juvenile Myoclonic Epilepsy

Fri, 05/03/2024 - 18:00

Cogn Neurodyn. 2024 Apr;18(2):337-347. doi: 10.1007/s11571-022-09838-7. Epub 2022 Jul 7.


Juvenile myoclonic epilepsy (JME) as an idiopathic generalized epilepsy has been studied by many advanced neuroimaging techniques to elucidate its neuroanatomical basis and pathophysiological mechanisms. In this paper, we used co-activation patterns (CAPs) to explore the differences of dynamic brain activity changes in resting state between JME patients and healthy controls. 27 cases JME patients and 27 cases healthy of fMRI data were collected. The structural image data of the subjects were analyzed by voxel-based morphological analysis, and the regions with gray matter volume atrophy and high voxel were selected as the regions of interest. Further, the mean disease duration was used as boundary to divide the patients' data into the below-average time and the above-average time groups, which were defined as patient disease duration groups. And these data were used to construct CAPs and to compare changes in brain dynamics. It was found that the number of patterns occurrences and the possibility of switching between patterns were smaller than those in the healthy control, which indicated patients with damage to brain regions. For the patient time control group, the number of patterns occurrences and the possibility of switching between patterns were similar, while there was linear regression between the three values and disease duration. Collectively, this study provides important evidence for revealing the key brain regions of JME by studying the transformation between CAPs. Future studies could investigate the effects of receiving treatment on patient dynamic brain activity.

PMID:38699614 | PMC:PMC11061087 | DOI:10.1007/s11571-022-09838-7

Short-term blood pressure variability and brain functional network connectivity in older adults

Fri, 05/03/2024 - 18:00

Neuroimage Rep. 2024 Mar;4(1):100198. doi: 10.1016/j.ynirp.2024.100198. Epub 2024 Feb 14.


BACKGROUND: Blood pressure variability is increasingly linked with cerebrovascular disease and Alzheimer's disease, independent of mean blood pressure levels. Elevated blood pressure variability is also associated with attenuated cerebrovascular reactivity, which may have implications for functional hyperemia underpinning brain network connectivity. It remains unclear whether blood pressure variability is related to functional network connectivity. We examined relationships between beat-to-beat blood pressure variability and functional connectivity in brain networks vulnerable to aging and Alzheimer's disease.

METHODS: 53 community-dwelling older adults (mean [SD] age = 69.9 [7.5] years, 62.3% female) without history of dementia or clinical stroke underwent continuous blood pressure monitoring and resting state fMRI scan. Blood pressure variability was calculated as variability independent of mean. Functional connectivity was determined by resting state fMRI for several brain networks: default, salience, dorsal attention, fronto-parietal, and language. Multiple linear regression examined relationships between short-term blood pressure variability and functional network connectivity.

RESULTS: Elevated short-term blood pressure variability was associated with lower functional connectivity in the default network (systolic: standardized ß = -0.30 [95% CI -0.59, -0.01], p = .04). There were no significant associations between blood pressure variability and connectivity in other functional networks or between mean blood pressure and functional connectivity in any network.

DISCUSSION: Older adults with elevated short-term blood pressure variability exhibit lower resting state functional connectivity in the default network. Findings support the role of blood pressure variability in neurovascular dysfunction and Alzheimer's disease. Blood pressure variability may represent an understudied early vascular risk factor for neurovascular dysfunction relevant to Alzheimer's disease, with potential therapeutic implications.

PMID:38699510 | PMC:PMC11064972 | DOI:10.1016/j.ynirp.2024.100198

Baseline functional connectivity predicts who will benefit from neuromodulation: evidence from primary progressive aphasia

Fri, 05/03/2024 - 18:00

medRxiv [Preprint]. 2024 Apr 20:2024.04.19.24305354. doi: 10.1101/2024.04.19.24305354.


BACKGROUND: Identifying the characteristics of individuals who demonstrate response to an intervention allows us to predict who is most likely to benefit from certain interventions. Prediction is challenging in rare and heterogeneous diseases, such as primary progressive aphasia (PPA), that have varying clinical manifestations. We aimed to determine the characteristics of those who will benefit most from transcranial direct current stimulation (tDCS) of the left inferior frontal gyrus (IFG) using a novel heterogeneity and group identification analysis.

METHODS: We compared the predictive ability of demographic and clinical patient characteristics (e.g., PPA variant and disease progression, baseline language performance) vs. functional connectivity alone (from resting-state fMRI) in the same cohort.

RESULTS: Functional connectivity alone had the highest predictive value for outcomes, explaining 62% and 75% of tDCS effect of variance in generalization (semantic fluency) and in the trained outcome of the clinical trial (written naming), contrasted with <15% predicted by clinical characteristics, including baseline language performance. Patients with higher baseline functional connectivity between the left IFG (opercularis and triangularis), and between the middle temporal pole and posterior superior temporal gyrus, were most likely to benefit from tDCS.

CONCLUSIONS: We show the importance of a baseline 7-minute functional connectivity scan in predicting tDCS outcomes, and point towards a precision medicine approach in neuromodulation studies. The study has important implications for clinical trials and practice, providing a statistical method that addresses heterogeneity in patient populations and allowing accurate prediction and enrollment of those who will most likely benefit from specific interventions.

PMID:38699365 | PMC:PMC11065007 | DOI:10.1101/2024.04.19.24305354

Functional network organization is locally atypical in children and adolescents with congenital heart disease

Fri, 05/03/2024 - 18:00

medRxiv [Preprint]. 2024 Apr 20:2024.04.19.24306106. doi: 10.1101/2024.04.19.24306106.


Children and adolescents with congenital heart disease (CHD) frequently experience neurodevelopmental impairments that can impact academic performance, memory, attention, and behavioral function, ultimately affecting overall quality of life. This study aims to investigate the impact of CHD on functional brain network connectivity and cognitive function. Using resting-state fMRI data, we examined several network metrics across various brain regions utilizing weighted networks and binarized networks with both absolute and proportional thresholds. Regression models were fitted to patient neurocognitive exam scores using various metrics obtained from all three methods. Our results unveil significant differences in network connectivity patterns, particularly in temporal, occipital, and subcortical regions, across both weighted and binarized networks. Furthermore, we identified distinct correlations between network metrics and cognitive performance, suggesting potential compensatory mechanisms within specific brain regions.

PMID:38699341 | PMC:PMC11065028 | DOI:10.1101/2024.04.19.24306106

Detecting language network alterations in mild cognitive impairment using task-based fMRI and resting-state fMRI: A comparative study

Fri, 05/03/2024 - 18:00

Brain Behav. 2024 May;14(5):e3518. doi: 10.1002/brb3.3518.


OBJECTIVE: The objective of this study was to investigate the functional changes associated with mild cognitive impairment (MCI) using independent component analysis (ICA) with the word generation task functional magnetic resonance imaging (fMRI) and resting-state fMRI.

METHODS: In this study 17 patients with MCI and age and education-matched 17 healthy individuals as control group are investigated. All participants underwent resting-state fMRI and task-based fMRI while performing the word generation task. ICA was used to identify the appropriate independent components (ICs) and their associated networks. The Dice Coefficient method was used to determine the relevance of the ICs to the networks of interest.

RESULTS: IC-14 was found relevant to language network in both resting-state and task-based fMRI, IC-4 to visual, and IC-28 to dorsal attention network (DAN) in word generation task-based fMRI by Sorento-Dice Coefficient. ICA showed increased activation in language network, which had a larger voxel size in resting-state functional MRI than word generation task-based fMRI in the bilateral lingual gyrus. Right temporo-occipital fusiform cortex, right hippocampus, and right thalamus were also activated in the task-based fMRI. Decreased activation was found in DAN and visual network MCI patients in word generation task-based fMRI.

CONCLUSION: Task-based fMRI and ICA are more sophisticated and reliable tools in evaluation cognitive impairments in language processing. Our findings support the neural mechanisms of the cognitive impairments in MCI.

PMID:38698619 | DOI:10.1002/brb3.3518