Individual fMRI Research Articles

Assessment of Visual Processing in Scotopic Sensitivity Syndrome Using Functional Magnetic Resonance Imaging

Background: Scotopic Sensitivity Syndrome (SSS) is characterized by difficulties in reading, visual distortions, and discomfort, which can be alleviated with tinted lenses. The condition affects visual processing and brain function, making it essential to explore objective solutions through neuroimaging techniques.Objective: To evaluate the effect of color-tinted lenses on reading performance and neural activity using functional magnetic resonance imaging (fMRI) in individuals with SSS.Methods: This study involved 40 participants with SSS, aged 18 to 34 years (mean age 23.7 ± 8.4), without neurological or psychiatric disorders. Participants were assessed for reading performance with and without color-tinted lenses. Reading speed in words per minute (WPM) was measured, and fMRI scans were performed before and after using lenses. Data were analyzed using the Wilcoxon signed-rank test, and brain activations were assessed with family-wise error correction at p < 0.05.Results: Median reading speed improved from 134 WPM to 168 WPM after using tinted lenses (p < 0.002). fMRI showed significant activation in the left middle and superior temporal gyri with lenses (p < 0.05).Conclusion: Tinted lenses improved reading performance and modulated brain activity, suggesting a potential therapeutic role in managing SSS.

Dose-Dependent Target Engagement of a Clinical Intermittent Theta Burst Stimulation Protocol: An Interleaved Transcranial Magnetic Stimulation–Functional Magnetic Resonance Imaging Study in Healthy People

BackgroundIntermittent theta burst stimulation (iTBS) of the dorsolateral prefrontal cortex (DLPFC) is widely applied as a therapeutic intervention in mental health; however, the understanding of its mechanisms is still incomplete. Prior magnetic resonance imaging (MRI) studies have mainly used offline iTBS or short sequences in concurrent transcranial magnetic stimulation (TMS)–functional MRI (fMRI). This study investigated a full 600-stimuli iTBS protocol using interleaved TMS-fMRI in comparison with 2 control conditions in healthy subjects. MethodsIn a crossover design, 18 participants underwent 3 sessions of interleaved iTBS-fMRI: 1) the left DLPFC at 40% resting motor threshold (rMT) intensity, 2) the left DLPFC at 80% rMT intensity, and 3) the left primary motor cortex (M1) at 80% rMT intensity. We compared immediate blood oxygen level–dependent (BOLD) responses during interleaved iTBS-fMRI across these conditions including correlations between individual fMRI BOLD activation and iTBS-induced electric field strength at the target sites. ResultsWhole-brain analysis showed increased activation in several regions following iTBS. Specifically, the left DLPFC, as well as the bilateral M1, anterior cingulate cortex, and insula, showed increased activation during 80% rMT left DLPFC stimulation. Increased BOLD activity in the left DLPFC was observed with neither 40% rMT left DLPFC stimulation nor left M1 80% rMT iTBS, whereas activation in other regions was found to overlap between conditions. Of note, BOLD activation and electric field intensities were only correlated for M1 stimulation and not for the DLPFC conditions. ConclusionsThis interleaved TMS-fMRI study showed dosage- and target-specific BOLD activation during a 600-stimuli iTBS protocol in healthy individuals. Future studies may use our approach for investigating target engagement in clinical samples.

A benchmark of individual auto-regressive models in a massive fMRI dataset

Abstract Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyncrasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time-series recorded during a natural video-watching task. The best performing models were then analyzed in terms of their data requirements and scaling, subject specificity, and the space-time structure of their predicted dynamics. We found the Chebnets, a type of graph convolutional neural network, to be best suited for temporal BOLD auto-regression, closely followed by linear models. Chebnets demonstrated an increase in performance with increasing amounts of data, with no complete saturation at 9 h of training data. Good generalization to other kinds of video stimuli and to resting-state data marked the Chebnets’ ability to capture intrinsic brain dynamics rather than only stimulus-specific autocorrelation patterns. Significant subject specificity was found at short prediction time lags. The Chebnets were found to capture lower frequencies at longer prediction time lags, and the spatial correlations in predicted dynamics were found to match traditional functional connectivity networks. Overall, these results demonstrate that large individual functional magnetic resonance imaging (fMRI) datasets can be used to efficiently train purely individual auto-regressive models of brain activity, and that massive amounts of individual data are required to do so. The excellent performance of the Chebnets likely reflects their ability to combine spatial and temporal interactions on large time scales at a low complexity cost. The non-linearities of the models did not appear as a key advantage. In fact, surprisingly, linear versions of the Chebnets appeared to outperform the original non-linear ones. Individual temporal auto-regressive models have the potential to improve the predictability of the BOLD signal. This study is based on a massive, publicly-available dataset, which can serve for future benchmarks of individual auto-regressive modeling.

Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging.

Gambling disorder is associated with deficits in reward-based learning, but the underlying computational mechanisms are still poorly understood. Here, we examined this issue using a stationary reinforcement learning task in combination with computational modeling and functional resonance imaging (fMRI) in individuals that regular participate in gambling (n = 23, seven fulfilled one to three DSM 5 criteria for gambling disorder, sixteen fulfilled four or more) and matched controls (n = 23). As predicted, the gambling group exhibited substantially reduced accuracy, whereas overall response times (RTs) were not reliably different between groups. We then used comprehensive modeling using reinforcement learning drift diffusion models (RLDDMs) in combination with hierarchical Bayesian parameter estimation to shed light on the computational underpinnings of this performance deficit. In both groups, an RLDDM in which both non-decision time and decision threshold (boundary separation) changed over the course of the experiment accounted for the data best. The model showed good parameter and model recovery, and posterior predictive checks revealed that, in both groups, the model accurately reproduced the evolution of accuracies and RTs over time. Modeling revealed that, compared to controls, the learning impairment in the gambling group was linked to a more rapid reduction in decision thresholds over time, and a reduced impact of value-differences on the drift rate. The gambling group also showed shorter non-decision times. FMRI analyses replicated effects of prediction error coding in the ventral striatum and value coding in the ventro-medial prefrontal cortex, but there was no credible evidence for group differences in these effects. Taken together, our findings show that reinforcement learning impairments in disordered gambling are linked to both maladaptive decision threshold adjustments and a reduced consideration of option values in the choice process.

FL-41 Tint Reduces Activation of Neural Pathways of Photophobia in Patients with Chronic Ocular Pain

To assess the therapeutic effect of tinted lenses (FL-41) on photophobia and light-evoked brain activity using functional magnetic resonance imaging (fMRI) in individuals with chronic ocular surface pain. Prospective case series. 25 subjects from the Miami veterans affairs (VA) eye clinic were recruited based on the presence of chronic ocular pain, dry eye symptoms, and photophobia. Using a 3T MRI scanner, subjects underwent 2 fMRI scans using an event-related design based on light stimuli: one scan while wearing FL-41 lenses and one without. Unpleasantness ratings evoked by the light stimuli were collected after each scan. With FL-41 lenses, subjects reported decreased (n = 19), maintained (n = 2), or increased (n = 4) light-evoked unpleasantness ratings. Group analysis at baseline (no lens) revealed significant light evoked responses in bilateral primary somatosensory (S1), bilateral secondary somatosensory (S2), bilateral insula, bilateral frontal pole, visual, precuneus, paracingulate, and anterior cingulate cortices (ACC) as well as cerebellar vermis, bilateral cerebellar hemispheric lobule VI, and bilateral cerebellar crus I and II. With FL-41 lenses, light-evoked responses were significantly decreased in bilateral S1, bilateral S2, bilateral insular, right temporal pole, precuneus, ACC, and paracingulate cortices as well as bilateral cerebellar hemispheric lobule VI. FL-41 lenses modulated photophobia symptoms in some individuals with chronic ocular pain. In conjunction, FL-41 lenses decreased activation in cortical areas involved in processing affective and sensory-discriminative dimensions of pain. Further research into these relationships will advance the ability to provide precision therapy for individuals with ocular pain.

Resting‐state network connectivity changes in prodromal stage of Alzheimer’s disease

AbstractBackgroundAlzheimer Disease (AD), the most common type of dementia, is accompanied by a generalized loss of neurons and cause severe changes in brain’s structure and function. Mild Cognitive Impairment is associated with a higher risk of developing dementia. This clinical classification is particularly relevant because each subtype is linked to a presumed etiology, with the amnestic subtypes (aMCI) considered as a prodromal form of AD (Petersen et al., 2009, 2014). Resting state fMRI functional connectivity (FCrs‐fMRI) analysis is broadly used to detect brain network differences in neurodegenerative diseases such as AD.MethodForty‐five participants were evaluated: patients with aMCI (n = 17), patients with dementia due to AD (n = 14) and healthy controls (HC) (n = 14). All participants received an extensive neuropsychological battery and brain MRI scanning. Resting state functional scans consisted of 240 T2*‐weighted echo planar imaging volumes (repetition time 3000 ms; echo time 60 ms; ip angle 90°; 32 axial slices; matrix 64×64; voxel size 3.3×3.4×4 mm). Additionally, a high‐resolution T1‐weighted magnetization prepared rapid acquisition gradient echo image was acquired. Data analyses were performed using the Functional Connectivity Toolbox (CONN) for Matlab (https://www.nitrc.org/frs/) to identify large‐scale patterns of temporal signal‐intensity coherence, interpreted as functional connectivity (Beckmann et al., 2005). Regions of interests (ROIs) analysis was performed for each participants and among group. Spatial maps of the group independent component analysis were used in a linear model t against each individual fMRI data set.ResultThe Default Mode Network (DMN) showed two regions of lower functional connectivity (FC) in AD when comparing with HC within the precuneus (p<0.001 uncorrected). No regions of FC changes were found in the DMN when comparing aMCI with HC or AD.ConclusionThe changes in FC rsfMRI in adults with AD are specifically related to regions of the DMN.

Modeling the effects of transcranial magnetic stimulation on spatial attention

Objectives. Transcranial magnetic stimulation (TMS) has been widely used to modulate brain activity in healthy and diseased brains, but the underlying mechanisms are not fully understood. Previous research leveraged biophysical modeling of the induced electric field (E-field) to map causal structure–function relationships in the primary motor cortex. This study aims at transferring this localization approach to spatial attention, which helps to understand the TMS effects on cognitive functions, and may ultimately optimize stimulation schemes. Approach. Thirty right-handed healthy participants underwent a functional magnetic imaging (fMRI) experiment, and seventeen of them participated in a TMS experiment. The individual fMRI activation peak within the right inferior parietal lobule (rIPL) during a Posner-like attention task defined the center target for TMS. Thereafter, participants underwent 500 Posner task trials. During each trial, a 5-pulse burst of 10 Hz repetitive TMS (rTMS) was given over the rIPL to modulate attentional processing. The TMS-induced E-fields for every cortical target were correlated with the behavioral modulation to identify relevant cortical regions for attentional orientation and reorientation. Main results. We did not observe a robust correlation between E-field strength and behavioral outcomes, highlighting the challenges of transferring the localization method to cognitive functions with high neural response variability and complex network interactions. Nevertheless, TMS selectively inhibited attentional reorienting in five out of seventeen subjects, resulting in task-specific behavioral impairments. The BOLD-measured neuronal activity and TMS-evoked neuronal effects showed different patterns, which emphasizes the principal distinction between the neural activity being correlated with (or maybe even caused by) particular paradigms, and the activity of neural populations exerting a causal influence on the behavioral outcome. Significance. This study is the first to explore the mechanisms of TMS-induced attentional modulation through electrical field modeling. Our findings highlight the complexity of cognitive functions and provide a basis for optimizing attentional stimulation protocols.

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset

Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level. In this paper, we tested whether accurate brain decoding models can be trained entirely at the individual level. We trained several classifiers on a dense individual functional magnetic resonance imaging (fMRI) dataset for which six participants completed the entire Human Connectome Project (HCP) task battery >13 times over ten separate fMRI sessions. We evaluated nine decoding methods, from Support Vector Machines (SVM) and Multi-Layer Perceptron (MLP) to Graph Convolutional Neural Networks (GCN). All decoders were trained to classify single fMRI volumes into 21 experimental conditions simultaneously, using ∼7 h of fMRI data per participant. The best prediction accuracies were achieved with GCN and MLP models, whose performance (57–67 % accuracy) approached state-of-the-art accuracy (76 %) with models trained at the group level on >1 K hours of data from the original HCP sample. Our SVM model also performed very well (54–62 % accuracy). Feature importance maps derived from MLP —our best-performing model— revealed informative features in regions relevant to particular cognitive domains, notably in the motor cortex. We also observed that inter-subject classification achieved substantially lower accuracy than subject-specific models, indicating that our decoders learned individual-specific features. This work demonstrates that densely-sampled neuroimaging datasets can be used to train accurate brain decoding models at the individual level. We expect this work to become a useful benchmark for techniques that improve model generalization across multiple subjects and acquisition conditions.

Time discrimination and change detection could share a common brain network: findings of a task-based fMRI study.

Over the past few years, several studies have described the brain activation pattern related to both time discrimination (TD) and change detection processes. We hypothesize that both processes share a common brain network which may play a significant role in more complex cognitive processes. The main goal of this proof-of-concept study is to describe the pattern of brain activity involved in TD and oddball detection (OD) paradigms, and in processes requiring higher cognitive effort. We designed an experimental task, including an auditory test tool to assess TD and OD paradigms, which was conducted under functional magnetic resonance imaging (fMRI) in 14 healthy participants. We added a cognitive control component into both paradigms in our test tool. We used the general linear model (GLM) to analyze the individual fMRI data images and the random effects model for group inference. We defined the areas of brain activation related to TD and OD paradigms. We performed a conjunction analysis of contrast TD (task > control) and OD (task > control) patterns, finding both similarities and significant differences between them. We conclude that change detection and other cognitive processes requiring an increase in cognitive effort require participation of overlapping functional and neuroanatomical components, suggesting the presence of a common time and change detection network. This is of particular relevance for future research on normal cognitive functioning in the healthy population, as well as for the study of cognitive impairment and clinical manifestations associated with various neuropsychiatric conditions such as schizophrenia.

Botulinum toxin A decreases neural activity in pain-related brain regions in individuals with chronic ocular pain and photophobia.

To examine the effect of botulinum toxin A (BoNT-A) on neural mechanisms underlying pain and photophobia using functional magnetic resonance imaging (fMRI) in individuals with chronic ocular pain. Twelve subjects with chronic ocular pain and light sensitivity were recruited from the Miami Veterans Affairs eye clinic. Inclusion criteria were: (1) chronic ocular pain; (2) presence of ocular pain over 1 week recall; and (3) presence of photophobia. All individuals underwent an ocular surface examination to capture tear parameters before and 4-6 weeks after BoNT-A injections. Using an event-related fMRI design, subjects were presented with light stimuli during two fMRI scans, once before and 4-6 weeks after BoNT-A injection. Light evoked unpleasantness ratings were reported by subjects after each scan. Whole brain blood oxygen level dependent (BOLD) responses to light stimuli were analyzed. At baseline, all subjects reported unpleasantness with light stimulation (average: 70.8 ± 32.0). Four to six weeks after BoNT-A injection, unpleasantness scores decreased (48.1 ± 33.6), but the change was not significant. On an individual level, 50% of subjects had decreased unpleasantness ratings in response to light stimulation compared to baseline ("responders," n = 6), while 50% had equivalent (n = 3) or increased (n = 3) unpleasantness ("non-responders"). At baseline, several differences were noted between responders and non-responders; responders had higher baseline unpleasantness ratings to light, higher symptoms of depression, and more frequent use of antidepressants and anxiolytics, compared to non-responders. Group analysis at baseline displayed light-evoked BOLD responses in bilateral primary somatosensory (S1), bilateral secondary somatosensory (S2), bilateral anterior insula, paracingulate gyrus, midcingulate cortex (MCC), bilateral frontal pole, bilateral cerebellar hemispheric lobule VI, vermis, bilateral cerebellar crus I and II, and visual cortices. BoNT-A injections significantly decreased light evoked BOLD responses in bilateral S1, S2 cortices, cerebellar hemispheric lobule VI, cerebellar crus I, and left cerebellar crus II. BoNT-A responders displayed activation of the spinal trigeminal nucleus at baseline where non-responders did not. BoNT-A injections modulate light-evoked activation of pain-related brain systems and photophobia symptoms in some individuals with chronic ocular pain. These effects are associated with decreased activation in areas responsible for processing the sensory-discriminative, affective, dimensions, and motor responses to pain.

Variational relevance evaluation of individual fMRI data enables deconstruction of task-dependent neural dynamics

In neuroimaging research, univariate analysis has always been used to localize “representations” at the microscale, whereas network approaches have been applied to characterize transregional “operations”. How are representations and operations linked through dynamic interactions? We developed the variational relevance evaluation (VRE) method to analyze individual task fMRI data, which selects informative voxels during model training to localize the “representation”, and quantifies the dynamic contributions of single voxels across the whole-brain to different cognitive functions to characterize the “operation”. Using 15 individual fMRI data files for higher visual area localizers, we evaluated the characterization of selected voxel positions of VRE and revealed different object-selective regions functioning in similar dynamics. Using another 15 individual fMRI data files for memory retrieval after offline learning, we found similar task-related regions working in different neural dynamics for tasks with diverse familiarities. VRE demonstrates a promising horizon in individual fMRI research.

Investigating Compensatory Brain Activity in Older Adults with Subjective Cognitive Decline.

Preclinical Alzheimer's disease (AD) is one possible cause of subjective cognitive decline (SCD). Normal task performance despite ongoing neurodegeneration is typically considered as neuronal compensation, which is reflected by greater neuronal activity. Compensatory brain activity has been observed in frontal as well as parietal regions in SCD, but data are scarce, especially outside the memory domain. To investigate potential compensatory activity in SCD. Such compensatory activity is particularly expected in participants where blood-based biomarkers indicated amyloid positivity as this implies preclinical AD. 52 participants with SCD (mean age: 71.00±5.70) underwent structural and functional neuroimaging (fMRI), targeting episodic memory and spatial abilities, and a neuropsychological assessment. The estimation of amyloid positivity was based on plasma amyloid-β and phosphorylated tau (pTau181) measures. Our fMRI analyses of the spatial abilities task did not indicate compensation, with only three voxels exceeding an uncorrected threshold at p < 0.001. This finding was not replicated in a subset of 23 biomarker positive individuals. Our results do not provide conclusive evidence for compensatory brain activity in SCD. It is possible that neuronal compensation does not manifest at such an early stage as SCD. Alternatively, it is possible that our sample size was too small or that compensatory activity may be too heterogeneous to be detected by group-level statistics. Interventions based on the individual fMRI signal should therefore be explored.

Temporal hierarchy of cortical responses reflects core-belt-parabelt organization of auditory cortex in musicians.

Human auditory cortex (AC) organization resembles the core-belt-parabelt organization in nonhuman primates. Previous studies assessed mostly spatial characteristics; however, temporal aspects were little considered so far. We employed co-registration of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in musicians with and without absolute pitch (AP) to achieve spatial and temporal segregation of human auditory responses. First, individual fMRI activations induced by complex harmonic tones were consistently identified in four distinct regions-of-interest within AC, namely in medial Heschl's gyrus (HG), lateral HG, anterior superior temporal gyrus (STG), and planum temporale (PT). Second, we analyzed the temporal dynamics of individual MEG responses at the location of corresponding fMRI activations. In the AP group, the auditory evoked P2 onset occurred ~25ms earlier in the right as compared with the left PT and ~15ms earlier in the right as compared with the left anterior STG. This effect was consistent at the individual level and correlated with AP proficiency. Based on the combined application of MEG and fMRI measurements, we were able for the first time to demonstrate a characteristic temporal hierarchy ("chronotopy") of human auditory regions in relation to specific auditory abilities, reflecting the prediction for serial processing from nonhuman studies.

Functional Alignment-Auxiliary Generative Adversarial Network-Based Visual Stimuli Reconstruction via Multi-Subject fMRI.

Functional Magnetic Resonance Imaging (fMRI) provides more precise spatial and temporal information to reconstruct stimulus images than other technologies that can be used to measure the human brain's neural responses. The fMRI scans, however, generally show heterogeneity among different subjects. The majority of the existing methods aim primarily at mining correlations between stimuli and evoked brain activity, disregarding the heterogeneity among subjects. Therefore, this heterogeneity will impair the reliability and applicability of multi-subject decoding results, leading to sub-optimal results. The present paper proposes the functional alignment-auxiliary generative adversarial network (FAA-GAN) as a novel multi-subject approach for visual image reconstruction that employs functional alignment to alleviate the heterogeneity between subjects. Our proposed FAA-GAN includes three key components: 1) a generative adversarial network (GAN) module for reconstructing visual stimuli, which consists of a visual image encoder as the generator that uses a nonlinear network to convert stimuli images into an implicit representation and a discriminator that generates the images comparable to the original images in detail; 2) a multi-subject functional alignment module, which is used to precisely align the individual fMRI response space of each subject in a common space to reduce the heterogeneity among different subjects; and 3) a cross-modal hashing retrieval module used for similarity retrieval of two modalities of data, i.e., the visual images and the evoked brain responses. Experiments on real-world datasets show that our FAA-GAN method does better than other state-of-the-art deep learning-based reconstruction methods with fMRI.

Patterns of connectome variability in autism across five functional activation tasks: findings from the LEAP project

BackgroundAutism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioral, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterize heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks.MethodsAll analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism (n = 282) and typically developing (TD) controls (n = 221) between 6 and 30 years of age. We employed a novel task potency approach which combines the unique aspects of both resting state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioral data.ResultsDeviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p < 0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p < 0.002). The CCA identified significant and robust brain-behavior covariation between functional connectivity atypicality and autism-related behavioral features.ConclusionsIndividuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show how sophisticated modeling methods such as task potency and normative modeling can be used toward unravelling complex heterogeneous conditions like autism.

Attenuated interoceptive processing in individuals with major depressive disorder and high repetitive negative thinking

Repetitive negative thinking (RNT) is a transdiagnostic symptom associated with poor outcomes in major depressive disorder (MDD). MDD is characterized by altered interoception, which has also been associated with poor outcomes. The present study investigated whether RNT is directly associated with altered interoceptive processing. Interoceptive awareness toward the heart and stomach was probed on the Visceral Interoceptive Attention (VIA) task with fMRI in MDD individuals who were propensity-matched on the severity of depression and anxiety symptoms and relevant demographics but different in RNT intensity (High RNT [H-RNT, n = 48] & Low RNT [L-RNT, n = 49]), and in matched healthy volunteers (HC, n = 27). Both H-RNT and L-RNT MDD individuals revealed reduced stomach interoceptive processing compared to HC in the left medial frontal region and insular cortex (H-RNT: β = −1.04, L-RNT: β = −0.97), perirhinal cortex (H-RNT: β = −0.99, L-RNT: β = −1.03), and caudate nucleus (H-RNT: β = −1.06, L-RNT: β = −0.89). However, H-RNT was associated with decreased right medial temporal lobe activity including the hippocampus and amygdala during stomach interoceptive trials (β = −0.61) compared to L-RNT. Insular interoceptive processing was similar in H-RNT and L-RNT participants (β = −0.07, p = 0.92). MDD individuals with high RNT exhibited altered gastric interoceptive responses in brain areas that are important for associating the information with specific contexts and emotions. Attenuated interoceptive processing may contribute to RNT generation, non-adaptive information processing, action selection, and thus poor treatment outcome.

Static and dynamic functional connectivity variability of the anterior-posterior hippocampus with subjective cognitive decline

BackgroundSubjective cognitive decline (SCD) is a putative Alzheimer’s disease (AD) precursor without objective neuropsychological deficits. The hippocampus plays an important role in cognitive function and emotional responses and is generally aberrant in SCD. However, previous studies have mainly focused on static functional connectivity (sFC) by resting-state functional magnetic resonance imaging (fMRI) in SCD individuals, and it remains unclear whether hippocampal dynamic functional connectivity (dFC) changes exist in SCD and whether those changes are associated with subtle changes in cognitive function or affect.MethodsSeventy SCD patients and 65 healthy controls were recruited. Demographic data, comprehensive neuropsychology assessments, and resting-state fMRI data were collected. The bilateral anterior and posterior hippocampi were selected as seeds to investigate the static and dynamic functional connectivity alterations in SCD.ResultsCompared to healthy controls, subjects with SCD exhibited: (1) decreased sFC between the left caudal hippocampus and left precuneus; (2) decreased dFC variability between the bilateral caudal hippocampus and precuneus; (3) increased dFC variability between the bilateral rostral hippocampus and caudate nucleus; and (4) increased dFC variability between the left rostral hippocampus and left olfactory cortex. Additionally, the attention scores were positively correlated with dFC variability between the left posterior hippocampus and left precuneus, and the dFC variability between the bilateral anterior hippocampus and caudate nucleus was positively correlated with depression scores and negatively correlated with global cognition scores.ConclusionSCD individuals exhibited abnormal sFC and dFC in the anterior-posterior hippocampus, and abnormal dFC was more widespread than abnormal sFC. A combination of sFC and dFC provides a new perspective for exploring the brain pathophysiological mechanisms in SCD and offers potential neuroimaging biomarkers for the early diagnosis and intervention of AD.

Changes in Resting fMRI Networks in Patients with Severe Traumatic Brain Injury During Therapeutic Rhythmic Transcranial Magnetic Stimulation (Case Report)

Severe traumatic brain injury (TBI) accompanied by impaired consciousness manifesting as prolonged postcoma unawareness (PCU) is one of the current medical and social problems causing high morbidity and mortality worldwide. Difficult recovery of such patients necessitates the development of additional neurorehabilitation approaches, including neuromodulation methods, as well as the search for objective markers of treatment efficacy. Aim of the study: to evaluate the effect of therapeutic rhythmic transcranial magnetic stimulation (rTMS) on fMRI resting state networks (RSN) in PCU after severe TBI. Materials and Methods. We analyzed individual fMRI RSN in three patients with PCU before and after a course of rTMS performed at different timepoints after severe TBI and with different efficacy of treatment. We assessed the topography and quantitative characteristics of the networks (DMN, sensorimotor, control functions, left and right fronto-parietal, auditory, and speech) known to be most significant for recovery of consciousness. Results. We found a trend toward normalization of RSN topography as well as an increase in the integral index of network intensity in two of three patients with a distinct increase in consciousness after a course of rTMS. Conclusion. Using case observations, we have demonstrated the therapeutic efficacy of rTMS and feasibility of using fMRI RSN as a reliable diagnostic approach in PCU following severe TBI.

Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease.

Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson’s disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength.

.Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain–computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications.Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments.Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC).Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome.Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.