Functional Connectivity Indices Research Articles

Explaining recovery from coma with multimodal neuroimaging.

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Detecting soil hydrological connectivity in a badland area by fast field cycling nuclear magnetic resonance relaxometry

Detecting soil hydrological connectivity in a badland area by fast field cycling nuclear magnetic resonance relaxometry

Combined EEG-tDCS approach in resting state to reduce comorbid anxiety and depressive symptoms in affective disorders: A sham-controlled pilot study

Continuous challenges have been imposed on mental health science by Anxiety and Depression disorders as the most prevalent and debilitating psychiatric conditions worldwide. Pharmacologic and cognitive behavioral therapies, either alone or in combination, have been considered as the first-line therapies, however, resistant symptomatology is prevalent in comorbid conditions with symptoms remaining after interventions. The demand for new therapeutic solutions has given space to the development of non-invasive brain stimulation techniques (NIBS), and the transmagnetic direct current stimulation (tDCS) has been reported as a safe and well-tolerated technique for the treatment of several mental health conditions, including Anxiety and Depression disorders. Relying on quantitative electroencephalography(qEEG)- tDCS approach, the current study aims to inspect the effect of tDCS intervention on patients who suffer from anxiety-depression comorbidity, in particular, the impact of tDCS intervention on qEEG spectral power activity and resting-state connectivity organization during eyes closed and eyes open protocols. QEEG data were acquired from eight patients suffering from moderate to severe anxiety-depression comorbid symptoms along with poor coping skills to manage stress and negative affect. Twelve control subjects allocated in the control group exhibiting low to moderate symptoms in both anxiety and depression conditions went also through the qEEG data acquisition. In addition, a sham-controlled study was conducted, and the patient group went through resting-state qEEG-tDCS neuromodulation once a week for ten weeks. Various-stage qEEG recordings were performed to inspect the efficacy of tDCS treatment during the modulation of brain regions involved in the regulation of affective responses. Our results demonstrated that after tDCS neuromodulation, the patients' groups exhibited decreased absolute power abnormalities over the left anterior cingulate cortex and reduced abnormal activity in the alpha band over posterior regions; improved functional connectivity indexes; decreased anxiety and depressive scores while positive affect score was improved. Besides the promising improvements, our study did not find a significant tDCS effect on perceived stress and negative affect scores. Consistently, significant differences in absolute spectral power over the left anterior cingulate cortex were detected among the patient group, as compared to the controls, as expected. Therefore, our study offers preliminary data to understand the neuroplasticity changes that potentially result from the manipulation of cortical excitability during affective regulation protocols followed by the consequent decrease of comorbid anxiety and depressive symptomatology. The pilot study was followed by prospective registration with ChiCTR2200062142.

Executive functions-based reading training engages the cingulo-opercular and dorsal attention networks.

The aim of this study was to determine the effect of a computerized executive functions (EFs)-based reading intervention on neural circuits supporting EFs and visual attention. Seed-to-voxel functional connectivity analysis was conducted focusing on large-scale attention system brain networks, during an fMRI reading fluency task. Participants were 8- to 12-year-old English-speaking children with dyslexia (n = 43) and typical readers (n = 36) trained on an EFs-based reading training (n = 40) versus math training (n = 39). Training duration was 8 weeks. After the EFs-based reading intervention, children with dyslexia improved their scores in reading rate and visual attention (compared to math intervention). Neurobiologically, children with dyslexia displayed an increase in functional connectivity strength after the intervention between the cingulo-opercular network and occipital and precentral regions. Noteworthy, the functional connectivity indices between these brain regions showed a positive correlation with speed of processing and visual attention scores in both pretest and posttest. The results suggest that reading improvement following an EFs-based reading intervention involves neuroplastic connectivity changes in brain areas related to EFs and primary visual processing in children with dyslexia. Our results highlight the need for training underlying cognitive abilities supporting reading, such as EFs and visual attention, in order to enhance reading abilities in dyslexia.

Changes in river systems and relevant hydrological responses in the Yangtze River Delta, China

River systems play an important role in the development of human society. However, they have been significantly affected by human activities, reducing the capacity for water supply, flood regulation, and aquatic ecosystem services. Identifying changes in river systems and hydrological responses to these changes is crucial for regional water management. In this study, based on the data derived from topographic maps of the 1960s, 1980s, and 2010s, we comprehensively quantified changes in river systems of the Yangtze River Delta (YRD), China. The results show that the density of rivers and the proportion of water surface decreased over the past several decades, while changes in the evolutionary coefficient of rivers and the ratio of area to length of rivers exhibited clear spatial differences. Low-grade rivers in urbanized areas have declined severely, leading to a simplification of river structure. Furthermore, the hydrological response to changes in the river system was revealed in the Taihu Lake Plain, an area of the YRD with a plain river network. A longitudinal functional connectivity index (LFCI) was adopted to assess the hydrological dynamics in water level, and it was found to have an increasing trend. In addition, the specific storage capacity (SSC) and the specific regulation capacity (SRC) were established to reveal the impact of changes in the structure and connectivity of the river system on the regulation and storage capacity, and both indicators exhibited a downward trend. Simulations based on the MIKE 11 model show that the reduction of tributaries across the region can result in an earlier peak timing and higher peak water level. Our results can support the development of river system protection and flood adaptation strategies in the delta plains.

Characterizing multiple sclerosis disease progression using a combined structural and functional connectivity metric

PurposeA combined resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) metric called structural and functional connectivity index (SFCI) was recently proposed for tracking disease status and progression in multiple sclerosis (MS). The metric combines fcMRI and transverse diffusivity (TD) along different functional pathways involved in principle symptomatic domains of MS.In a longitudinal study of patients with MS receiving the same MS therapy, initial worsening of transcallosal (TC) motor and frontoparietal (FP) cognitive networks, as measured by fcMRI and DTI over the first year was followed by stabilization in the second year of follow-up. In this study we have (i) probed relationships between individual and composite neurological measures of MS with SFCI and its individual components along TC motor and FP cognitive pathways and (ii) compared sensitivity of SFCI to treatment-induced longitudinal changes with each individual imaging measure. MethodsTwenty five patients with MS (15 female, age 42 ± 8 y) participated in this study and were scanned at 3 T whole body MRI scanner with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at baseline and 6, 12, 18 and 24 months after starting fingolimod. fcMRI and TD along TC and FP pathways were combined to form structural and functional connectivity index (SFCI) at each time point. Correlations between individual/combined neurological measures and individual imaging components/SFCI at baseline and were evaluated and compared. In addition, efficacies of individual and combined imaging metrics in tracking network integrity were compared. ResultsIndividual TD along the TC pathway was significantly inversely correlated with all individual/composite neurological scores. There were moderate correlations of TC and FP components of SFCI with most of the neurological scores, and the pathway-combined SFCI was significantly correlated with all neurological scores. Trend-level increases of both TC and FP fcMRI were observed during the second year of follow-up, both TC and FP TD increased significantly in the first year and then stabilized during the second year. A trend toward a decrease in combined imaging metrics along TC and FP were observed during the first year, followed by a trend toward an increase in these metrics during the second year, while a significant decrease in SFCI during the first year followed by a significant increase during the second year was observed. ConclusionsSFCI was more effective in tracking network integrity/disease progression than individual pathway-specific components, which supports its use as an imaging marker for MS disease status and progression.

Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors

Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear. Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics. Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p < 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann–Whitney U test with Benjamini–Hochberg correction procedure and use of a false discovery rate of 5%. Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.

A New Model for Solving Hydrological Connectivity Inside Soils by Fast Field Cycling NMR Relaxometry

In this paper, a new quantitative approach for estimating the structural and functional connectivity inside soil by Fast Field Cycling (FFC) NMR relaxometry is presented, tested by measurements carried out in three samples with different texture characteristics. Measurements by FFC NMR relaxometry have been carried out using water-suspended samples and Proton Larmor frequencies (νL) ranging in the 0.015–35 MHz interval. Two non-degraded soil samples, with different textural characteristics, and a degraded soil collected in a badland area, were analyzed. For a given soil and any applied Proton Larmor frequency, the distribution of the longitudinal relaxation times, T1, (i.e., relaxogram) measured by FFC NMR has been integrated, and the resulting S-shaped curve (i.e., relaxogram integration curve) was represented, for the first time, by Gumbel’s diagram. This new representation of the relaxogram integration curve, transforming the S-shaped curve into a straight line, allowed for distinguishing three linear components, corresponding to three different relaxation time ranges, characterized by three different slopes. Two points, identified by the abrupt slope changes of the relaxogram integration curve plotted in Gumbel’s diagram, are used to identify two characteristic values of relaxation time, T1A and T1B, which define three well-known pore size classes (T1 &lt; T1A micro-pores, T1A &lt; T1 &lt; T1B meso-pores, and T1 &gt; T1B macro-pores). The relaxogram integration curve allowed for calculating the non-exceeding empirical cumulative frequency, F(T1), corresponding to the characteristic T1A and T1B values. The analysis demonstrated that the relaxogram can be used to determine the pore-size ranges of each investigated sample. Finally, using the slope values of the three components of the relaxogram integration curve, a new definition of the Structural Connectivity Index, SCI, and Functional Connectivity Index, FCI, was proposed.

Brainprint based on functional connectivity and asymmetry indices of brain regions: A case study of biometric person identification with non‐expensive electroencephalogram headsets

AbstractBrain‐computer interface applications for biometric person identification have increased their interest in recent years since they are potentially more secure and more difficult to counterfeit than traditional biometric techniques. However, it is necessary to consider how brain waves are acquired for this purpose, not only in terms of efficiency but also of practical comfort for the user and the affordability degree of the biosignal acquisition device so that their everyday application can become a realistic possibility. In this context, this paper presents the capabilities of using a non‐expensive wireless electroencephalogram (EEG) device to extract spectral‐related and functional connectivity information of brain activity. The proposed method achieved a sufficient biometric identification with two datasets of 13 and 109 subjects when comparing the performance of a sizeable classification algorithm set. In addition, a novel feature in EEG biometric identification, called asymmetry index, is introduced here. Furthermore, this is the first study in this field to consider the effect of the time‐lapse between different recording sessions on the system's behaviour when using a low‐cost EEG device with identification accuracy rates of up to 100%.

Brainstem–cortex disconnection in amyotrophic lateral sclerosis: bulbar impairment, genotype associations, asymptomatic changes and biomarker opportunities

BackgroundBulbar dysfunction is a cardinal feature of ALS with important quality of life and management implications. The objective of this study is the longitudinal evaluation of a large panel imaging metrics pertaining to bulbar dysfunction, encompassing cortical measures, structural and functional cortico-medullary connectivity indices and brainstem metrics.MethodsA standardised, multimodal imaging protocol was implemented with clinical and genetic profiling to systematically appraise the biomarker potential of specific metrics. A total of 198 patients with ALS and 108 healthy controls were included.ResultsLongitudinal analyses revealed progressive structural and functional disconnection between the motor cortex and the brainstem over time. Cortical thickness reduction was an early feature on cross-sectional analyses with limited further progression on longitudinal follow-up. Receiver operating characteristic analyses of the panel of MR metrics confirmed the discriminatory potential of bulbar imaging measures between patients and controls and area-under-the-curve values increased significantly on longitudinal follow-up. C9orf72 carriers exhibited lower brainstem volumes, lower cortico-medullary structural connectivity and faster cortical thinning. Sporadic patients without bulbar symptoms, already exhibit significant brainstem and cortico-medullary connectivity alterations.DiscussionOur results indicate that ALS is associated with multi-level integrity change from cortex to brainstem. The demonstration of significant corticobulbar alterations in patients without bulbar symptoms confirms considerable presymptomatic disease burden in sporadic ALS. The systematic assessment of radiological measures in a single-centre academic study helps to appraise the diagnostic and monitoring utility of specific measures for future clinical and clinical trial applications.

Spatio-Temporal Evolution Patterns of Hydrological Connectivity of Wetland Biodiversity Hotspots in Sanjiang Plain between 1995 and 2015

Hydrological connectivity is the main non-biological driving factor of wetland ecological processes and is key to maintaining the stability and biodiversity of the whole ecosystem. Socio-economic activities have had a significant impact on the hydrological connectivity of wetlands, resulting in the loss of biodiversity and the degradation of the ecological functions of wetlands. Wetland biodiversity hotspots in Sanjiang Plain that were identified in the previous literature using the Systematic Conservation Planning (SCP) method were chosen as the research objects. The SCP method was combined with the structural hydrological connectivity index (Integral Index of Connectivity (IIC) and Probability of Connectivity (PC)) and the functional hydrological connectivity index (Morphological Spatial Pattern Analysis) to analyze the spatio-temporal changes in the hydrological connectivity of the wetland biodiversity hotspots in Sanjiang Plain. The results showed that the hydrological connectivity within the eight identified wetland biodiversity hotspots in Sanjiang Plain experienced varying degrees of decline in the period between 1995 and 2015. Structurally, the IIC values of wetlands in all of the biodiversity hotspots were more than 0.5, and the PC values were more than 0.9, but most of the hotspots showed declining trends of varying degrees from 2010 to 2015. Functionally, the average proportion of core wetlands in the hotspots has decreased by 4.82%, and the average proportion of edge wetlands has increased by 2.71% over the last 20 years. The findings on the hydrological connectivity evolution patterns can aid in the conservation and restoration of wetlands and biodiversity hotspots.

New indicator of habitat functionality reveals high risk of underestimating trade-offs among sustainable development goals: The case of wild reindeer and hydropower

Although biodiversity is crucial for Sustainable Development Goals (SDGs), following the current trajectory, we risk failing SDG 15. Using a new indicator quantifying the loss of functional habitat (habitat that is simultaneously suitable and well-connected), we show that the real impact of renewable energy is far larger than previously assumed. Specifically, we estimate that the construction of hydropower reservoirs in south Norway caused a loss of ca. 222 km2 of functional habitat for wild reindeer (Rangifer tarandus)—which is far larger than assumed based on land inundation indices (110 km2). Fully mitigating these impacts is challenging: scenario analyses reveal that the measures proposed by societal actors would yield only a fraction of the habitat lost (2–12 km2) and could cause trade-off risks with other SDGs. Using indices of functional connectivity is crucial for environmental impact assessments, as entire ecological networks for several species can be affected far beyond the reservoirs.

The promising combination of a remote sensing approach and landscape connectivity modelling at a fine scale in urban planning

Urban landscapes are rapid changing ecosystems with diverse urban forms that impede the movement of organisms. Therefore, designing and modelling ecological networks to identify biodiversity reservoirs and their corridors are crucial aspects of land management in terms of population persistence and survival. However, the land cover/use maps used for landscape connectivity modelling can lack information in such a highly complex environment. In this context, remote sensing approaches are gaining interest for the development of accurate land cover/use maps. We tested the efficiency of an object-based classification using open-source projects and free images to identify vegetation strata at a very fine scale and evaluated its contribution to landscape connectivity modelling. We compared different spatial and thematic resolutions from existing databases and object-based image analyses in three French cities. Our results suggested that this remote sensing approach produced reliable land cover maps to differentiate artificial areas, tree vegetation and herbaceous vegetation. Land cover maps enhanced with the remote sensing products substantially changed the structural connectivity indices, showing an improvement up to four times the proportion of herbaceous and tree vegetation. In addition, functional connectivity indices evaluated for several forest species were mainly impacted for medium dispersers in quantitative (metrics) and qualitative (corridors) estimations. Thus, the combination of this reproductible remote sensing approach and landscape connectivity modelling at a very fine scale provides new insights into the characterisation of ecological networks for conservation planning.

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders

BackgroundExternalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs.MethodsMulti-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6–18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive–compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined.ResultsNo significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results.ConclusionsThe current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations.

Functional connectivity in the central executive network predicts changes in binge drinking behavior during emerging adulthood: an observational prospective study.

Binge drinking contributes to the immense public health burden associated with alcohol use, especially among younger drinkers. Little is known about the underlying neurobiology of changes in this behavior over time. This preliminary study aimed to identify neurobiological markers of binge drinking behavior change during emerging adulthood. Observational prospective investigation of neurobiological predictors of binge drinking behavior. Communities surrounding a large, public university in the northeastern United States. A total of 42 emerging adults (48% female), approximately half meeting criteria for an alcohol use disorder. Past month binge drinking, the dependent variable, was assessed at two time-points (T1, T2) via self-report. Ten indices of resting-state functional connectivity within the central executive network (CEN), a brain network involved in executive function, were collected at T1 and specified as independent variables in cross-sectional and prospective Poisson models. All models controlled for age, sex, and alcohol use disorder status. The cross-sectional model yielded five significant associations between CEN connectivity and binge drinking incidence. Connections anchored primarily in the anterior CEN exhibited negative associations with binge drinking incidence (P = 0.001, 0.004, 0.011), and connections stemming from the right posterior parietal cortex exhibited positive associations with binge drinking incidence (P = 0.041, 0.045). In prospective models, stronger frontoparietal connectivity between the right dorsolateral prefrontal cortex and left posterior parietal cortex predicted greater increases in binge drinking incidence over time (P = 0.003). There is an association between central executive network connectivity and heavy drinking, as well as evidence that functional pathways within the central executive network may contribute to changes in problematic drinking behaviors.

Altered Intra- and Inter-Network Functional Connectivity in Patients With Crohn's Disease: An Independent Component Analysis-Based Resting-State Functional Magnetic Resonance Imaging Study.

BackgroundMany studies have reported changes in the structure and function of several brain areas in patients with Crohn’s disease (CD). However, little is known about whether the possible functional connectivity of resting-state networks (RSNs) is altered in CD patients.PurposeAim to investigate the intra- and inter-network alterations between related RSNs in patients with CD and the potential relationships between altered neuroimaging and CD clinical indices.Materials and MethodsIn this study, 20 CD patients and 22 age- and sex-matched healthy controls were included. All participants underwent functional magnetic resonance imaging examination. We used independent component analysis (ICA) to explore the changes in RSNs and evaluated functional connectivity between different RSNs using functional network connectivity (FNC) analysis, and Pearson correlation analysis was performed between altered intra- and inter-network functional connectivity and CD clinical index.ResultsSix CD-related RSNs were identified via ICA, namely the high visual, prime visual, language, dorsal default mode, posterior insula, and precuneus networks. Compared to healthy controls, patients with CD showed significant changes in prime visual and language networks. Additionally, the functional connectivity (FC) values of the left calcarine within the prime visual network were negatively correlated with CD duration. The inter-alterations showed that a significantly increased FNC existed between the language and dorsal default mode networks.ConclusionThe results showed CD-related changes in brain functional networks. This evidence provides more insights into the pathophysiological mechanisms of brain plasticity in CD.

Dynamic functional brain network connectivity during pseudoword processing relates to children's reading skill

Learning to read requires children to link print (orthography) with its corresponding speech sounds (phonology). Yet, most EEG studies of reading development focus on emerging functional specialization (e.g., developing increasingly refined orthographic representations), rather than directly measuring the functional connectivity that links orthography and phonology in real time. In this proof-of-concept study we relate children's reading skill to both orthographic specialization for print (via the N170, also called the N1, event related potential, ERP) and orthographic-phonological integration (via dynamic/event-related EEG phase synchronization – an index of functional brain network connectivity). Typically developing English speaking children (n = 24; 4–14 years) and control adults (n = 20; 18–35 years) viewed pseudowords, consonants and unfamiliar false fonts during a 1-back memory task while 64-channel EEG was recorded. Orthographic specialization (larger N170 for pseudowords vs. false fonts) became more left-lateralized with age, but not with reading skill. Conversely, children's reading skill correlated with functional brain network connectivity during pseudoword processing that requires orthography-phonology linking. This was seen during two periods of simultaneous low frequency synchronization/high frequency desynchronization of posterior-occipital brain network activity. Specifically, in stronger readers, left posterior-occipital activity showed more delta (1–3Hz) synchronization around 300–500 ms (simultaneous with gamma 30–80 Hz desynchronization) and more gamma desynchronization around 600–1000 ms (simultaneous with theta 3–7Hz synchronization) during pseudoword vs. false font processing. These effects were significant even when controlling for age (moderate – large effect sizes). Dynamic functional brain network connectivity measures the brain's real-time sound-print linking. It may offer an under-explored, yet sensitive, index of the neural plasticity associated with reading development.

Association of Cholinergic Basal Forebrain Volume and Functional Connectivity with Markers of Inflammatory Response in the Alzheimer's Disease Spectrum.

Inflammation has been described as a key pathogenic event in Alzheimer's disease (AD), downstream of amyloid and tau pathology. Preclinical and clinical data suggest that the cholinergic basal forebrain may moderate inflammatory response to different pathologies. To study the association of cholinergic basal forebrain volume and functional connectivity with measures of neuroinflammation in people from the AD spectrum. We studied 261 cases from the DELCODE cohort, including people with subjective cognitive decline, mild cognitive impairment, AD dementia, first degree relatives, and healthy controls. Using Bayesian ANCOVA, we tested associations of MRI indices of cholinergic basal forebrain volume and functional connectivity with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglia activation, and serum levels of complement C3. Using Bayesian elastic net regression, we determined associations between basal forebrain measures and a large inflammation marker panel from CSF and serum. We found anecdotal to moderate evidence in favor of the absence of an effect of basal forebrain volume and functional connectivity on CSF sTREM2 and serum C3 levels both in Aβ42/ptau-positive and negative cases. Bayesian elastic net regression identified several CSF and serum markers of inflammation that were associated with basal forebrain volume and functional connectivity. The effect sizes were moderate to small. Our data-driven analyses generate the hypothesis that cholinergic basal forebrain may be involved in the neuroinflammation response to Aβ42 and phospho-tau pathology in people from the AD spectrum. This hypothesis needs to be tested in independent samples.

Predicting Fluid Intelligence via Naturalistic Functional Connectivity Using Weighted Ensemble Model and Network Analysis

Objectives: Functional connectivity triggered by naturalistic stimuli (e.g., movie clips), coupled with machine learning techniques provide great insight in exploring brain functions such as fluid intelligence. However, functional connectivity is multi-layered while traditional machine learning is based on individual model, which is not only limited in performance, but also fails to extract multi-dimensional and multi-layered information from the brain network. Methods: In this study, inspired by multi-layer brain network structure, we propose a new method, namely weighted ensemble model and network analysis, which combines machine learning and graph theory for improved fluid intelligence prediction. Firstly, functional connectivity analysis and graphical theory were jointly employed. The functional connectivity and graphical indices computed using the preprocessed fMRI data were then all fed into an auto-encoder parallelly for automatic feature extraction to predict the fluid intelligence. In order to improve the performance, tree regression and ridge regression models were stacked and fused automatically with weighted values. Finally, layers of auto-encoder were visualized to better illustrate the connectome patterns, followed by the evaluation of the performance to justify the mechanism of brain functions. Results: Our proposed method achieved the best performance with a 3.85 mean absolute deviation, 0.66 correlation coefficient and 0.42 R-squared coefficient; this model outperformed other state-of-the-art methods. It is also worth noting that the optimization of the biological pattern extraction was automated though the auto-encoder algorithm. Conclusion: The proposed method outperforms the state-of-the-art reports, also is able to effectively capture the biological patterns of functional connectivity during a naturalistic movie state for potential clinical explorations.

Type 1 diabetes affects the brain functional connectivity underlying working memory processing.

Visuospatial working memory (VSWM) deficits have been demonstrated to occur during the development of type-1-diabetes (T1D). Despite confirming the early appearance of distinct task-related brain activation patterns in T1D patients compared to healthy controls, the effect of VSWM load on functional brain connectivity during task performance is still unknown. Using electroencephalographic methods, the present study evaluated this topic in clinically well-controlled T1D young patients and healthy individuals, while they performed a VSWM task with different memory load levels during two main VSWM processing phases: encoding and maintenance. The results showed a significantly lower number of correct responses and longer reaction times in T1D while performing the task. Besides, higher and progressively increasing functional connectivity indices were found for T1D patients in response to cumulative degrees of VSWM load, from the beginning of the VSWM encoding phase, without notably affecting the VSWM maintenance phase. In contrast, healthy controls managed to solve the task, showing lower functional brain connectivity during the initial VSWM processing steps with more gradual task-related adjustments. Present results suggest that T1D patients anticipate high VSWM load demands by early recruiting supplementary processing resources as the probable expression of a more inefficient, though paradoxically better adjusted to task demands cognitive strategy.