Functional Regions Of Interest Research Articles

Predicting high-level visual areas in the absence of task fMRI.

The ventral visual stream is organized into units, or functional regions of interest (fROIs), specialized for processing high-level visual categories. Task-based fMRI scans ("localizers") are typically used to identify each individual's nuanced set of fROIs. The unique landscape of an individual's functional activation may rely in large part on their specialized connectivity patterns; recent studies corroborate this by showing that connectivity can predict individual differences in neural responses. We focus on the ventral visual stream and ask: how well can an individual's resting state functional connectivity localize their fROIs for face, body, scene, and object perception? And are the neural processors for any particular visual category better predicted by connectivity than others, suggesting a tighter mechanistic relationship between connectivity and function? We found, among 18 fROIs predicted from connectivity for each subject, all but one were selective for their preferred visual category. Defining an individual's fROIs based on their connectivity patterns yielded regions that were more selective than regions identified from previous studies or atlases in nearly all cases. Overall, we found that in the absence of a domain-specific localizer task, a 10-min resting state scan can be reliably used for defining these fROIs.

Diffusion tensor imaging identifies atrial regional changes associated with atrial fibrillation remodelling

Abstract Background Structural remodeling in the context of persistent atrial fibrillation (PAF) has been mainly characterized by imaging techniques focused on fibrosis detection. Diffusion tensor imaging (DTI) has demonstrated its direct relationship with histological properties and myocyte organization, which may provide a more complete structural information. However, the potential of DTI in the characterization of the AF-related structural substrate remains unknown. Purposes To evaluate the performance of DTI-derived parameters to characterise the structural remodelling derived from persistent AF and its relationship with specific atrial regions associated with AF maintenance (i.e., driver regions). Methods Two experimental groups of pigs were studied: control (n=7) and PAF (n=10). The PAF swine model was developed using rapid atrial pacing until the animals reached a stage of self-sustained PAF. The AV node was ablated to prevent heart failure due to fast ventricular activation rates. A second lead in the right ventricular was used for ventricular pacing at 60-110 bpm. Animals with PAF were studied after ≥ 6 months in self-sustained persistent AF. AF animals underwent invasive electroanatomical to identify driver regions based on instantaneous frequency modulation maps. After euthanasia, explanted hearts from AF animals and controls underwent DTI including 15 diffusion weighted images with a b-value of 600 s/mm² plus a non-DWI volume (isotropic resolution, 0.6 mm³). DTI images were segmented and divided into 13 atrial anatomical regions. Driver regions in AF animals were also segmented and classified as functional regions of interest (ROIs) (Figure 2B). Afterwards, diffusion tensors were estimated and scalar features were analyzed (Figures 1A and 2A) according to both anatomical and functional ROIs. Results DTI analysis in AF animals revealed region-specific structural remodelling (Figure 1B). Compared to controls, PAF animals showed an increased fractional anisotropy (FA) in both right and left atrial free walls (Figure 1C). Other anatomical regions, such as the superior vena cava (SVC), the coronary sinus (CS) or the left atrium roof developed higher axial diffusivities in AF compared to controls (Figure 1D), without significant alterations in FA (Figure 1C). AF driver regions were frequently located in the CS, the SVC and the LAA (Figure 2C). AF driver ROIs into the CS and the SVC showed intracase increased diffusivities and decreased linear indexes compared to non-driver regions. However, AF driver ROIs in the LAA showed increased intervoxel main eigenvector dispersion in contrast to non-driver ROIs (Figure 2C). Conclusion DTI sequences provide atrial structural information associated with functional tissue properties relevant for AF maintenance.

A personalized cortical atlas for functional regions of interest.

Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.

Bilateral Representation of Sensorimotor Responses in Benign Adult Familial Myoclonus Epilepsy: An MEG Study.

Patients with cortical reflex myoclonus manifest typical neurophysiologic characteristics due to primary sensorimotor cortex (S1/M1) hyperexcitability, namely, contralateral giant somatosensory-evoked potentials/fields and a C-reflex (CR) in the stimulated arm. Some patients show a CR in both arms in response to unilateral stimulation, with about 10-ms delay in the non-stimulated compared with the stimulated arm. This bilateral C-reflex (BCR) may reflect strong involvement of bilateral S1/M1. However, the significance and exact pathophysiology of BCR within 50 ms are yet to be established because it is difficult to identify a true ipsilateral response in the presence of the giant component in the contralateral hemisphere. We hypothesized that in patients with BCR, bilateral S1/M1 activity will be detected using MEG source localization and interhemispheric connectivity will be stronger than in healthy controls (HCs) between S1/M1 cortices. We recruited five patients with cortical reflex myoclonus with BCR and 15 HCs. All patients had benign adult familial myoclonus epilepsy. The median nerve was electrically stimulated unilaterally. Ipsilateral activity was investigated in functional regions of interest that were determined by the N20m response to contralateral stimulation. Functional connectivity was investigated using weighted phase-lag index (wPLI) in the time-frequency window of 30–50 ms and 30–100 Hz. Among seven of the 10 arms of the patients who showed BCR, the average onset-to-onset delay between the stimulated and the non-stimulated arm was 8.4 ms. Ipsilateral S1/M1 activity was prominent in patients. The average time difference between bilateral cortical activities was 9.4 ms. The average wPLI was significantly higher in the patients compared with HCs in specific cortico-cortical connections. These connections included precentral-precentral, postcentral-precentral, inferior parietal (IP)-precentral, and IP-postcentral cortices interhemispherically (contralateral region-ipsilateral region), and precentral-IP and postcentral-IP intrahemispherically (contralateral region-contralateral region). The ipsilateral response in patients with BCR may be a pathologically enhanced motor response homologous to the giant component, which was too weak to be reliably detected in HCs. Bilateral representation of sensorimotor responses is associated with disinhibition of the transcallosal inhibitory pathway within homologous motor cortices, which is mediated by the IP. IP may play a role in suppressing the inappropriate movements seen in cortical myoclonus.

An isotropic EPI database and analytical pipelines for rat brain resting-state fMRI

Resting-state functional magnetic resonance imaging (fMRI) has drastically expanded the scope of brain research by advancing our knowledge about the topologies, dynamics, and interspecies translatability of functional brain networks. Several databases have been developed and shared in accordance with recent key initiatives in the rodent fMRI community to enhance the transparency, reproducibility, and interpretability of data acquired at various sites. Despite these pioneering efforts, one notable challenge preventing efficient standardization in the field is the customary choice of anisotropic echo planar imaging (EPI) schemes with limited spatial coverage. Imaging with anisotropic resolution and/or reduced brain coverage has significant shortcomings including reduced registration accuracy and increased deviation in brain feature detection. Here we proposed a high-spatial-resolution (0.4 mm), isotropic, whole-brain EPI protocol for the rat brain using a horizontal slicing scheme that can maintain a functionally relevant repetition time (TR), avoid high gradient duty cycles, and offer unequivocal whole-brain coverage. Using this protocol, we acquired resting-state EPI fMRI data from 87 healthy rats under the widely used dexmedetomidine sedation supplemented with low-dose isoflurane on a 9.4 T MRI system. We developed an EPI template that closely approximates the Paxinos and Watson's rat brain coordinate system and demonstrated its ability to improve the accuracy of group-level approaches and streamline fMRI data pre-processing. Using this database, we employed a multi-scale dictionary-learning approach to identify reliable spatiotemporal features representing rat brain intrinsic activity. Subsequently, we performed k-means clustering on those features to obtain spatially discrete, functional regions of interest (ROIs). Using Euclidean-based hierarchical clustering and modularity-based partitioning, we identified the topological organizations of the rat brain. Additionally, the identified group-level FC network appeared robust across strains and sexes. The “triple-network” commonly adapted in human fMRI were resembled in the rat brain. Through this work, we disseminate raw and pre-processed isotropic EPI data, a rat brain EPI template, as well as identified functional ROIs and networks in standardized rat brain coordinates. We also make our analytical pipelines and scripts publicly available, with the hope of facilitating rat brain resting-state fMRI study standardization.

Transcutaneous auricular vagus nerve stimulation (taVNS) for migraine: an fMRI study

BackgroundDysfunction of the thalamocortical connectivity network is thought to underlie the pathophysiology of the migraine. This current study aimed to explore the thalamocortical connectivity changes during 4 weeks of continuous...

Regional imaging genetic enrichment analysis.

Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals. In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. The R code and sample data are freely available at https://github.com/lshen/RIGEA. Supplementary data are available at Bioinformatics online.

Functional Parcellation of the Speech Production Cortex.

Neuroimaging has revealed a core network of cortical regions that contribute to speech production, but the functional organization of this network remains poorly understood. Purpose We describe efforts to identify reliable boundaries around functionally homogenous regions within the cortical speech motor control network in order to improve the sensitivity of functional magnetic resonance imaging (fMRI) analyses of speech production and thus improve our understanding of the functional organization of speech production in the brain. Method We used a bottom-up, data-driven approach by pooling data from 12 previously conducted fMRI studies of speech production involving the production of monosyllabic and bisyllabic words and pseudowords that ranged from single vowels and consonant-vowel pairs to short sentences (163 scanning sessions, 136 unique participants, 39 different speech conditions). After preprocessing all data through the same pipeline and registering individual contrast maps to a common surface space, hierarchical clustering was applied to contrast maps randomly sampled from the pooled data set in order to identify consistent functional boundaries across subjects and tasks. Boundary completion was achieved by applying adaptive smoothing and watershed segmentation to the thresholded population-level boundary map. Hierarchical clustering was applied to the mean within-functional region of interest (fROI) response to identify networks of fROIs that respond similarly during speech. Results We identified highly reliable functional boundaries across the cortical areas involved in speech production. Boundary completion resulted in 117 fROIs in the left hemisphere and 109 in the right hemisphere. Clustering of the mean within-fROI response revealed a core sensorimotor network flanked by a speech motor planning network. The majority of the left inferior frontal gyrus clustered with the visual word form area and brain regions (e.g., anterior insula, dorsal anterior cingulate) associated with detecting salient sensory inputs and choosing the appropriate action. Conclusion The fROIs provide insight into the organization of the speech production network and a valuable tool for studying speech production in the brain by improving within-group and between-groups comparisons of speech-related brain activity. Supplemental Material https://doi.org/10.23641/asha.9402674.

Mining Regional Imaging Genetic Associations via Voxel-wise Enrichment Analysis.

Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful annotation and modest dimensionality compared with voxel-wise approaches. Typical ROI-level measures used in these studies are summary statistics from voxel-wise measures in the region, without making full use of individual voxel signals. In this paper, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxel-wise enrichment analysis, which embraces the collective effect of weak voxel-level signals within an ROI. We demonstrate our method on an imaging genetic analysis using data from the Alzheimers Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxel-wise FDGPET measures between 116 ROIs and 19 AD candidate SNPs. Compared with traditional ROI-wise and voxel-wise approaches, our method identified 102 additional significant associations, some of which were further supported by evidences in brain tissue-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain.

Relationship Between Neuroanatomical and Serotonergic Hypotheses of Obsessive-Compulsive Disorder: A Combined Functional Magnetic Resonance Imaging-Evoked Potential Study.

The so-called neuroanatomical hypothesis (with an increased activity of orbitofrontal cortex [OFC]) and the serotonergic hypothesis (with low activity in this system) have been discussed regarding the pathogenesis of obsessive-compulsive disorder (OCD) for decades. This study aimed to look for a relationship between the 2 pathogenetic concepts. Nineteen OCD patients (8 female, 11 male, mean ± SD age = 33.37 ± 11.73 years, Yale-Brown Obsessive Compulsive Scale: 21.79 ± 6.59; diagnosed by ICD-10/DSM-IV-TR) were compared to 19 matched healthy controls (8 female, 11 male, mean ± SD age = 31.63 ± 10.79 years) and investigated (2012-2014) with the loudness dependence of auditory-evoked potentials (LDAEP) as a marker of the synaptic serotonergic activity and functional magnetic resonance imaging (fMRI) during the delay discounting paradigm, inducing OFC blood-oxygen level-dependent activity in the 2 groups. There were significant correlation coefficients between LDAEP (eLORETA right side) and fMRI OFC activities (anatomic region of interest) within the delay discounting paradigm (immediate vs control) in patients with OCD (r = -0.554; P = .014). LDAEP differed between the 2 groups with larger LDAEP at Cz in OCD patients indicating low serotonergic activity (0.28 ± 0.14 vs 0.20 ± 0.10 µV/10 dB, F₂,₃₅ = 4.66, P = .016). fMRI activations of dorsolateral and medial prefrontal cortex as well as ventral striatum (functional region of interest) were different between OCD and healthy volunteers. The 2 main pathophysiologic hypotheses of OCD seem to be related to each other as measured by LDAEP and fMRI OFC activity during the delay discounting task. This could be interpreted as a further hint that low serotonergic activity induces altered OFC responsivity, which has to be treated in each patient with OCD by a serotonin agonist.

Framework for reanalysis of publicly available Affymetrix\xae GeneChip\xae data sets based on functional regions of interest

BackgroundSince the introduction of microarrays in 1995, researchers world-wide have used both commercial and custom-designed microarrays for understanding differential expression of transcribed genes. Public databases such as ArrayExpress and the Gene Expression Omnibus (GEO) have made millions of samples readily available. One main drawback to microarray data analysis involves the selection of probes to represent a specific transcript of interest, particularly in light of the fact that transcript-specific knowledge (notably alternative splicing) is dynamic in nature.ResultsWe therefore developed a framework for reannotating and reassigning probe groups for Affymetrix® GeneChip® technology based on functional regions of interest. This framework addresses three issues of Affymetrix® GeneChip® data analyses: removing nonspecific probes, updating probe target mapping based on the latest genome knowledge and grouping probes into gene, transcript and region-based (UTR, individual exon, CDS) probe sets. Updated gene and transcript probe sets provide more specific analysis results based on current genomic and transcriptomic knowledge. The framework selects unique probes, aligns them to gene annotations and generates a custom Chip Description File (CDF). The analysis reveals only 87% of the Affymetrix® GeneChip® HG-U133 Plus 2 probes uniquely align to the current hg38 human assembly without mismatches. We also tested new mappings on the publicly available data series using rat and human data from GSE48611 and GSE72551 obtained from GEO, and illustrate that functional grouping allows for the subtle detection of regions of interest likely to have phenotypical consequences.ConclusionThrough reanalysis of the publicly available data series GSE48611 and GSE72551, we profiled the contribution of UTR and CDS regions to the gene expression levels globally. The comparison between region and gene based results indicated that the detected expressed genes by gene-based and region-based CDFs show high consistency and regions based results allows us to detection of changes in transcript formation.

Maximized likelihood ratio tests for functional localization in fMRI

fMRI localizer tasks are often used to define subject specific functional regions of interest (fROIs) that contain the relevant features for subsequent analyses. fROIs are typically small and show large interindividual differences in extent and effect size. As statistical testing procedures focus on con- trolling false positives, this may lead to an ad-hoc adjustment of thresholding in some individuals. The promising likelihood ratio (LR) testing approach for fMRI (Kang et al. 2015) provides simultaneous control of both false positives and negatives by contrasting evidence in favor of true activation against evidence in favor of the null hypothesis. The authors propose to estimate the expected alternative by a percentile (e.g. 95th) across the voxels of an effect size map. However, in the context of fROIs, pre-defined observed percentiles may induce inconsistent activation across subjects. In this study we show the potential of a maximized LR approach (Bickel, 2012) for this particular application. The maximum LR is calculated over the same interval of functionally relevant alternatives for all subjects, enabling consistent localization of the fROIs in both subjects with low levels and high levels of general activity.

Activation of the Human MT Complex by Motion in Depth Induced by a Moving Cast Shadow

A moving cast shadow is a powerful monocular depth cue for motion perception in depth. For example, when a cast shadow moves away from or toward an object in a two-dimensional plane, the object appears to move toward or away from the observer in depth, respectively, whereas the size and position of the object are constant. Although the cortical mechanisms underlying motion perception in depth by cast shadow are unknown, the human MT complex (hMT+) is likely involved in the process, as it is sensitive to motion in depth represented by binocular depth cues. In the present study, we examined this possibility by using a functional magnetic resonance imaging (fMRI) technique. First, we identified the cortical regions sensitive to the motion of a square in depth represented via binocular disparity. Consistent with previous studies, we observed significant activation in the bilateral hMT+, and defined functional regions of interest (ROIs) there. We then investigated the activity of the ROIs during observation of the following stimuli: 1) a central square that appeared to move back and forth via a moving cast shadow (mCS); 2) a segmented and scrambled cast shadow presented beside the square (sCS); and 3) no cast shadow (nCS). Participants perceived motion of the square in depth in the mCS condition only. The activity of the hMT+ was significantly higher in the mCS compared with the sCS and nCS conditions. Moreover, the hMT+ was activated equally in both hemispheres in the mCS condition, despite presentation of the cast shadow in the bottom-right quadrant of the stimulus. Perception of the square moving in depth across visual hemifields may be reflected in the bilateral activation of the hMT+. We concluded that the hMT+ is involved in motion perception in depth induced by moving cast shadow and by binocular disparity.

Functional Regions of Interest in Electrical Impedance Tomography: A Secondary Analysis of Two Clinical Studies.

IntroductionPatients with acute respiratory distress syndrome (ARDS) typically show a high degree of ventilation inhomogeneity, which is associated with morbidity and unfavorable outcomes. Electrical impedance tomography (EIT) is able to detect ventilation inhomogeneity, but it is unclear which method for defining the region of interest (ROI) should be used for this purpose. The aim of our study was to compare the functional region of interest (fROI) method to both the lung area estimation method (LAEM) and no ROI when analysing global parameters of ventilation inhomogeneity. We assumed that a good method for ROI determination would lead to a high discriminatory power for ventilation inhomogeneity, as defined by the area under the receiver operating characteristics curve (AUC), comparing patients suffering from ARDS and control patients without pulmonary pathologies.MethodsWe retrospectively analysed EIT data from 24 ARDS patients and 12 control patients without pulmonary pathology. In all patients, a standardized low-flow-pressure volume maneuver had been performed and was used for EIT image generation. We compared the AUC for global inhomogeneity (GI) index and coefficient of variation (CV) between ARDS and control patients using all EIT image pixels, the fROI method and the LAEM for ROI determination.ResultsWhen analysing all EIT image pixels, we found an acceptable AUC both for the GI index (AUC = 0.76; 95% confidence interval (CI) 0.58–0.94) and the CV (AUC = 0.74; 95% CI 0.55–0.92). With the fROI method, we found a deteriorating AUC with increasing threshold criteria. With the LAEM, we found the best AUC both for the GI index (AUC = 0.89; 95% CI 0.78–1.0) and the CV (AUC = 0.89; 95% CI 0.78–1.0) using a threshold criterion of 50% of the maximum tidal impedance change.ConclusionIn the assessment of ventilation inhomogeneity with EIT, functional regions of interest obscure the difference between patients with ARDS and control patients without pulmonary pathologies. The LAEM is preferable to the fROI method when assessing ventilation inhomogeneity.

A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest

The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts’ knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.

The Effect of Disruption of Prefrontal Cortical Function with Transcranial Magnetic Stimulation on Visual Working Memory.

It is proposed that feedback signals from the prefrontal cortex (PFC) to extrastriate cortex are essential for goal-directed processing, maintenance, and selection of information in visual working memory (VWM). In a previous study, we found that disruption of PFC function with transcranial magnetic stimulation (TMS) in healthy individuals impaired behavioral performance on a face/scene matching task and decreased category-specific tuning in extrastriate cortex as measured with functional magnetic resonance imaging (fMRI). In this study, we investigated the effect of disruption of left inferior frontal gyrus (IFG) function on the fidelity of neural representations of two distinct information codes: (1) the stimulus category and (2) the goal-relevance of viewed stimuli. During fMRI scanning, subjects were presented face and scene images in pseudo-random order and instructed to remember either faces or scenes. Within both anatomical and functional regions of interest (ROIs), a multi-voxel pattern classifier was used to quantitatively assess the fidelity of activity patterns representing stimulus category: whether a face or a scene was presented on each trial, and goal relevance, whether the presented image was task relevant (i.e., a face is relevant in a “Remember Faces” block, but irrelevant in a “Remember Scenes” block). We found a reduction in the fidelity of the stimulus category code in visual cortex after left IFG disruption, providing causal evidence that lateral PFC modulates object category codes in visual cortex during VWM. In addition, we found that IFG disruption caused a reduction in the fidelity of the goal relevance code in a distributed set of brain regions. These results suggest that the IFG is involved in determining the task-relevance of visual input and communicating that information to a network of regions involved in further processing during VWM. Finally, we found that participants who exhibited greater fidelity of the goal relevance code in the non-disrupted right IFG after TMS performed the task with the highest accuracy.

Hippocampal-thalamic wiring in medial temporal lobe epilepsy: Enhanced connectivity per hippocampal voxel.

Medial temporal lobe epilepsy (TLE) with hippocampal sclerosis is often accompanied by widespread changes in ipsilateral and contralateral white matter connectivity. Recent studies have proposed that patients may show pathologically enhanced wiring of the limbic circuits. To better address this issue, we specifically probed connection patterns between hippocampus and thalamus and examined their impact on cognitive function. A group of 44 patients with TLE (22 with right and 22 with left hippocampal sclerosis) and 24 healthy control participants were examined with high-resolution T1 imaging, memory functional magnetic resonance imaging (fMRI) and probabilistic diffusion tractography. Thirty-four patients had further extensive neuropsychological testing. After whole brain segmentation with FreeSurfer, tractography streamline samples were drawn with hippocampus as the seed and thalamus as the target region. Two tractography strategies were applied: The first targeted the anatomic thalamic volume segmented in FreeSurfer and the second a functional region of interest in themediodorsal thalamus derived from the activation during delayed recognition memory. We found a pronounced enhancement of connectivity between the sclerotic hippocampus and the ipsilateral thalamus both in the right and left TLE as compared to healthy control participants. This finding held for both the anatomically and the functionally defined thalamic target. Although differences were apparent in the number of absolute fibers, they were most pronounced when correcting for hippocampal volume. In terms of cognitive function, the number of hippocampal-thalamic connections was negatively correlated with performance in a variety of executive tasks, notably in the Trail Making Test, thus suggesting that the pathologic wiring did not compensate cognitive curtailing. We suggest that TLE is accompanied by an abnormal and dysfunctional enhancement of connectivity between the hippocampus and the thalamus, which is maximal on the side of the sclerosis. This pathologic pattern of limbic wiring might reflect structural remodeling along common pathways of seizure propagation.

Mapping Thalamocortical Functional Connectivity in Chronic and Early Stages of Psychotic Disorders

There is considerable evidence that the thalamus is abnormal in psychotic disorders. Resting-state functional magnetic resonance imaging has revealed an intriguing pattern of thalamic dysconnectivity in psychosis characterized by reduced prefrontal cortex (PFC) connectivity and increased somatomotor-thalamic connectivity. However, critical knowledge gaps remain with respect to the onset, anatomical specificity, and clinical correlates of thalamic dysconnectivity in psychosis. Resting-state functional magnetic resonance imaging was collected on 105 healthy subjects and 148 individuals with psychosis, including 53 early-stage psychosis patients. Using all 253 subjects, the thalamus was parceled into functional regions of interest (ROIs) on the basis of connectivity with six a priori defined cortical ROIs covering most of the cortical mantle. Functional connectivity between each cortical ROI and its corresponding thalamic ROI was quantified and compared across groups. Significant differences in the ROI-to-ROI analysis were followed up with voxelwise seed-based analyses to further localize thalamic dysconnectivity. ROI analysis revealed reduced PFC-thalamic connectivity and increased somatomotor-thalamic connectivity in both chronic and early-stage psychosis patients. PFC hypoconnectivity and motor cortex hyperconnectivity correlated in patients, suggesting that they result from a common pathophysiological mechanism. Seed-based analyses revealed thalamic hypoconnectivity in psychosis localized to dorsolateral PFC, medial PFC, and cerebellar areas of the well-described executive control network. Across all subjects, thalamic connectivity with areas of the fronto-parietal network correlated with cognitive functioning, including verbal learning and memory. Thalamocortical dysconnectivity is present in both chronic and early stages of psychosis, includes reduced thalamic connectivity with the executive control network, and is related to cognitive impairment.

Locally-Optimized Inter-Subject Alignment of Functional Cortical Regions

Inter-subject registration of cortical areas is necessary in functional imaging (fMRI) studies for making inferences about equivalent brain function across a population. However, many high-level visual brain areas are defined as peaks of functional contrasts and it is usually difficult to identify clear anatomical landmarks and boundaries for these areas, due to large variability in their cortical position. As a consequence, previous methods usually fail to accurately map such functional regions of interest (ROIs) across participants. To address this problem, we propose a locally optimized registration method that directly predicts the location of a seed ROI on a separate target cortical sheet by maximizing the functional correlation between regions and simultaneously constraining the global structure of the mapping, while allowing for non-local deformations in its topology. Our registration method outperformed two canonical baselines (anatomical landmark-based AFNI alignment and cortical curvature-based FreeSurfer alignment) in the percentage of overlap between predicted region and ground truth LOC. Furthermore, the maps obtained using our method are more consistent across subjects than both baseline measures. Consequently, our method has the ability to directly and immediately improve the quality of group maps for high-level visual areas in countless fMRI studies. This would dramatically increase the statistical power of such studies, as a more accurate mapping to a common space implies less smoothing and larger effect sizes.

Manual versus automatic segmentation of functional regions of interest: Effects on multi-voxel pattern analysis and repetition suppression

Manual versus automatic segmentation of functional regions of interest: Effects on multi-voxel pattern analysis and repetition suppression