Imaging studies have provided evidence that cognitive-behavioral therapy (CBT) is able to change brain activation in phobic patients in response to threatening stimuli. The changes occurred in both emotion-generating and modulatory regions. In this study, we use a data-driven approach to explore resting state cerebral blood flow (CBF) measured by arterial spin labeling (ASL), before and after CBT.

In the present multi-modal study we aimed to investigate the role of visual exploration in relation to the neuronal activity and performance during visuospatial processing. To this end, event related functional magnetic resonance imaging er-fMRI was combined with simultaneous eye tracking recording and transcranial magnetic stimulation (TMS). Two groups of twenty healthy subjects each performed an angle discrimination task with different levels of difficulty during er-fMRI. The number of fixations as a measure of visual exploration effort was chosen to predict blood oxygen level-dependent (BOLD) signal changes using the general linear model (GLM). Without TMS, a positive linear relationship between the visual exploration effort and the BOLD signal was found in a bilateral fronto-parietal cortical network, indicating that these regions reflect the increased number of fixations and the higher brain activity due to higher task demands. Furthermore, the relationship found between the number of fixations and the performance demonstrates the relevance of visual exploration for visuospatial task solving. In the TMS group, offline theta bursts TMS (TBS) was applied over the right posterior parietal cortex (PPC) before the fMRI experiment started. Compared to controls, TBS led to a reduced correlation between visual exploration and BOLD signal change in regions of the fronto-parietal network of the right hemisphere, indicating a disruption of the network. In contrast, an increased correlation was found in regions of the left hemisphere, suggesting an intent to compensate functionality of the disturbed areas. TBS led to fewer fixations and faster response time while keeping accuracy at the same level, indicating that subjects explored more than actually needed.

In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

While voxel-based 3-D MRI analysis methods as well as assessment of subtracted ictal versus interictal perfusion studies (SISCOM) have proven their potential in the detection of lesions in focal epilepsy, a combined approach has not yet been reported. The present study investigates if individual automated voxel-based 3-D MRI analyses combined with SISCOM studies contribute to an enhanced detection of mesiotemporal epileptogenic foci. Seven consecutive patients with refractory complex partial epilepsy were prospectively evaluated by SISCOM and voxel-based 3-D MRI analysis. The functional perfusion maps and voxel-based statistical maps were coregistered in 3-D space. In five patients with temporal lobe epilepsy (TLE), the area of ictal hyperperfusion and corresponding structural abnormalities detected by 3-D MRI analysis were identified within the same temporal lobe. In two patients, additional structural and functional abnormalities were detected beyond the mesial temporal lobe. Five patients with TLE underwent epileptic surgery with favourable postoperative outcome (Engel class Ia and Ib) after 3-5 years of follow-up, while two patients remained on conservative treatment. In summary, multimodal assessment of structural abnormalities by voxel-based analysis and SISCOM may contribute to advanced observer-independent preoperative assessment of seizure origin.

Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurological outcome. RS-fMRI was used to evaluate functional and effective connectivity within the default mode network in a cohort of 90 comatose patients and their impact on functional neurological outcome after 3 months. The RS-fMRI processing protocol comprises the evaluation of functional and effective connectivity within the default mode network. Seed-to-voxel and ROI-to-ROI feature analysis was performed as starting point for a supervised machine-learning approach. Classification of the Cerebral Performance Category (CPC) 1-3 (good to acceptable outcome) versus CPC 4-5 (adverse outcome) achieved a positive predictive value of 91.7%, sensitivity of 90.2%, and accuracy of 87.8%. A direct link to the level of consciousness and outcome after 3 months was identified for measures of segregation in the precuneus, in medial and right frontal regions. Thalamic connectivity appeared significantly reduced in patients without conscious response. Decreased within-network connectivity in the default mode network and within cortico-thalamic circuits correlated with clinical outcome after 3 months. Our results indicate a potential role of these markers for decision-making in comatose patients early after cardiac arrest.

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.

Schizophrenia is characterized by deficits in gesturing that is important for nonverbal communication. Research in healthy participants and brain-damaged patients revealed a left-lateralized fronto-parieto-temporal network underlying gesture performance. First evidence from structural imaging studies in schizophrenia corroborates these results. However, as of yet, it is unclear if cortical thickness abnormalities contribute to impairments in gesture performance. We hypothesized that patients with deficits in gesture production show cortical thinning in 12 regions of interest (ROIs) of a gesture network relevant for gesture performance and recognition. Forty patients with schizophrenia and 41 healthy controls performed hand and finger gestures as either imitation or pantomime. Group differences in cortical thickness between patients with deficits, patients without deficits, and controls were explored using a multivariate analysis of covariance. In addition, the relationship between gesture recognition and cortical thickness was investigated. Patients with deficits in gesture production had reduced cortical thickness in eight ROIs, including the pars opercularis of the inferior frontal gyrus, the superior and inferior parietal lobes, and the superior and middle temporal gyri. Gesture recognition correlated with cortical thickness in fewer, but mainly the same, ROIs within the patient sample. In conclusion, our results show that impaired gesture production and recognition in schizophrenia is associated with cortical thinning in distinct areas of the gesture network.

Motor hand skill and associated dexterity is important for meeting the challenges of daily activity and an important resource post-stroke. In this context, the present study investigated the finger movements of right-handed subjects during tactile manipulation of a cuboid, a prototypical task underlying tactile exploration. During one motor act, the thumb and fingers of one hand surround the cuboid in a continuous and regular manner. While the object is moved by the guiding thumb, the opposed supporting fingers are replaced once they reach their joint limits by free fingers, a mechanism termed finger gaiting.

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.

The perception of faces and consequent social inferences are fundamental for interpersonal communication. While facial expression is important for interindividual communication, constitutional and acquired features are crucial for basic emotions of attraction or repulsion. An emotional bias in face processing has been shown in schizophrenia, but the neurobiological mechanisms are unclear. Studies on the interaction between face processing and the emotional state of healthy individuals may help to elucidate the pathogenesis of the paranoid syndrome in psychosis. This study addressed facial attractiveness and paranoid ideas in a non-clinical population. Using functional magnetic resonance imaging (fMRI), we investigated neural activation patterns of 99 healthy subjects during the passive perception of a dynamic presentation of faces with different attractiveness. We found that the perceived attractiveness of faces was linked to the activity of face processing and limbic regions including the fusiform gyrus, amygdala, and prefrontal areas. Paranoid beliefs interacted with perceived attractiveness in these regions resulting in a higher response range and increased activation after the presentation of unattractive faces. However, no behavioral interactions between reported subjective attractiveness and paranoid beliefs were found. The results showed that increased activation of limbic brain regions is linked to paranoid beliefs. Since similar correlations were found in clinical populations with paranoid syndromes, we suggest a dimension of emotional dysregulation ranging from subclinical paranoid beliefs to paranoid schizophrenia.

Optimism bias, i.e. expecting the future to hold more desirable than undesirable outcomes, also extends to people that we like or admire. However, it remains unknown how the brain generates this social optimism bias. In this study, respondents estimated the likelihood of future desirable and undesirable outcomes for an in-group and three out-groups: warm-incompetent, cold-competent, and cold-incompetent. We found a strong social optimism bias for the in-group and the warm out-group and an inverted pattern for the cold-incompetent out-group. For all groups, scores of social optimism bias correlated with the brain activity in structures that respondents differentially engaged depending on the target social group. In line with our hypotheses, evaluating the in-group recruited the ventromedial prefrontal cortex and the precuneus/posterior cingulate cortex, whereas evaluating the warm out-group engaged the posterior insula, mid cingulate cortex, and somatosensory cortices. These findings suggest different underlying cognitive mechanisms of social optimism bias for these groups, despite similar behavioural patterns. Thinking about the cold out-groups recruited the right anterior temporal lobe, and temporoparietal junction. Evaluating the cold-incompetent out-group additionally recruited the anterior insula, inferior frontal cortex and dorsomedial frontal cortex. We discuss these neuroimaging findings with respect to their putative cognitive functions.

The changing MRI signal accompanying brain maturation in fetal brains can be quantified on apparent diffusion coefficient (ADC) maps. Deviations from the natural course of ADC values may reflect structural pathology. The purpose of this study was to determine the influence of fetal pathologies on the ADC values in different regions of the fetal brain and their evolution with increasing gestational age.

Episodic memory is the memory for experienced events. A peak competence of episodic memory is the mental combination of events to infer commonalities. Inferring commonalities may proceed with and without consciousness of events. Yet what distinguishes conscious from unconscious inference? This question inspired nine experiments that featured strongly and weakly masked cartoon clips presented for unconscious and conscious inference. Each clip featured a scene with a visually impenetrable hiding place. Five animals crossed the scene one-by-one consecutively. One animal trajectory represented one event. The animals moved through the hiding place, where they might linger or not. The participants' task was to observe the animals' entrances and exits to maintain a mental record of which animals hid simultaneously. We manipulated information load to explore capacity limits. Memory of inferences was tested immediately, 3.5 or 6 min following encoding. The participants retrieved inferences well when encoding was conscious. When encoding was unconscious, the participants needed to respond intuitively. Only habitually intuitive decision makers exhibited a significant delayed retrieval of inferences drawn unconsciously. Their unconscious retrieval performance did not drop significantly with increasing information load, while conscious retrieval performance dropped significantly. A working memory network, including hippocampus, was activated during both conscious and unconscious inference and correlated with retrieval success. An episodic retrieval network, including hippocampus, was activated during both conscious and unconscious retrieval of inferences and correlated with retrieval success. Only conscious encoding/retrieval recruited additional brain regions outside these networks. Hence, levels of consciousness influenced the memories' behavioral impact, memory capacity, and the neural representational code.

To develop individualized motor rehabilitation, knowledge of the relationship between neuroplastic reorganization and motor recovery after pediatric arterial ischemic stroke (AIS) is crucial. Thus, we investigated functional connectivity in patients after AIS with good motor outcome and in patients with hemiparesis compared with typically developing peers. We included 18 patients (n = 9 with hemiparesis, n = 9 with good motor outcome) with pediatric AIS in the chronic phase (≥ 2 years after diagnosis, diagnosed > 16 years) and 18 peers matched by age and gender. Participants underwent a standardized motor assessment, single-pulse transcranial magnetic stimulation to determine the type of corticospinal tract wiring, and resting-state functional magnetic resonance imaging to examine motor network connectivity. Corticospinal tract wiring was contralateral in all participants. Patients with hemiparesis had lower interhemispheric connectivity strength compared with patients with good clinical outcome and peers. Patients with good clinical outcome had higher intrahemispheric connectivity strength compared with peers. Further, higher intrahemispheric connectivity was related to better motor outcome in patients. Our findings suggest that better motor outcome after pediatric AIS is related to higher motor network connectivity strength. Thus, resting-state functional connectivity might be predictive for motor recovery after pediatric AIS.

Temporal lobe epilepsy (TLE) is associated with brain pathology extending beyond temporal lobe structures. We sought to look for informative patterns of brain tissue properties in TLE that go beyond the established morphometry differences. We hypothesised that volume differences, particularly in hippocampus, will be paralleled by changes in brain microstructure. The cross-sectional study included TLE patients (n = 25) from a primary care center and sex-/age-matched healthy controls (n = 55). We acquired quantitative relaxometry-based magnetic resonance imaging (MRI) data yielding whole-brain maps of grey matter volume, magnetization transfer (MT) saturation, and effective transverse relaxation rate R2* indicative for brain tissue myelin and iron content. For statistical analysis, we used the computational anatomy framework of voxel-based morphometry and voxel-based quantification. There was a positive correlation between seizure activity and MT saturation measures in the ipsilateral hippocampus, paralleled by volume differences bilaterally. Disease duration correlated positively with iron content in the mesial temporal lobe, while seizure freedom was associated with a decrease of iron in the very same region. Our findings demonstrate the link between TLE clinical phenotype and brain anatomy beyond morphometry differences to show the impact of disease burden on specific tissue properties. We provide direct evidence for the differential effect of clinical phenotype characteristics on processes involving tissue myelin and iron in mesial temporal lobe structures. This study offers a proof-of-concept for the investigation of novel imaging biomarkers in focal epilepsy.

Neurological soft signs (NSS) are related to grey matter and functional brain abnormalities in schizophrenia. Studies in healthy subjects suggest, that NSS are also linked to white matter. However, the association between NSS and white matter abnormalities in schizophrenia remains to be elucidated. The present study investigated, if NSS are related to white matter alterations in patients with schizophrenia. The total sample included 42 healthy controls and 41 patients with schizophrenia. We used the Neurological Evaluation Scale (NES), and we acquired diffusion weighted magnetic resonance imaging to assess white matter on a voxel-wise between subject statistic. In patients with schizophrenia, linear associations between NES with fractional anisotropy (FA), radial, axial, and mean diffusivity were analyzed with tract-based spatial statistics while controlling for age, medication dose, the severity of the disease, and motion. The main pattern of results in patients showed a positive association of NES with all diffusion measures except FA in important motor pathways: the corticospinal tract, internal capsule, superior longitudinal fascicle, thalamocortical radiations and corpus callosum. In addition, exploratory tractography analysis revealed an association of the right aslant with NES in patients. These results suggest that specific white matter alterations, that is, increased diffusivity might contribute to NSS in patients with schizophrenia.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Electroconvulsive therapy (ECT) is a highly efficient treatment for depression. Previous studies repeatedly reported an ECT-induced volume increase in the hippocampi. We assume that this also affects extended hippocampal networks. This study aims to investigate the structural and functional interplay between hippocampi, hippocampal pathways and core regions of the default mode network (DMN). Twenty patients with a current depressive episode receiving ECT-treatment and twenty age and sex matched healthy controls (HC) were included in the study. ECT-patients underwent multimodal magnetic resonance imaging (MRI)-scans (diffusion weighted imaging, resting state functional MRI) before and after an ECT-index series. HC were also scanned twice in a similar between-scan time-interval. Parahippocampal cingulum (PHC) and uncinate fasciculus (UF) were reconstructed for each participant using manual tractography. Fractional anisotropy (FA) was averaged across tracts. Furthermore, we investigated seed-based functional connectivity (FC) from bilateral hippocampi and from the PCC, a core region of the DMN. At baseline, FA in PHC and UF did not differ between groups. There was no baseline group difference of hippocampal-FC. PCC-FC was decreased in ECT-patients. ECT induced a decrease in FA in the left PHC in the ECT group. No longitudinal changes of FA were found in the UF. Furthermore, there was a decrease in hippocampal-PCC-FC, an increase in hippocampal-supplementary motor area-FC, and an increase in PCC-FC in the ECT-group, reversing group differences at baseline. Our findings suggest that ECT induces structural and functional remodeling of a hippocampal-DMN. Those changes may contribute to ECT-induced clinical response in patients with depression.

Abnormal yawning is an underappreciated phenomenon in patients with ischemic stroke. We aimed at identifying frequently affected core regions in the supratentorial brain of stroke patients with abnormal yawning and contributing to the anatomical network concept of yawning control. Ten patients with acute anterior circulation stroke and ≥3 yawns/15 min without obvious cause were analyzed. The NIH stroke scale (NIHSS), Glasgow Coma Scale (GCS), symptom onset, period with abnormal yawning, blood oxygen saturation, glucose, body temperature, blood pressure, heart rate, and modified Rankin scale (mRS) were assessed for all patients. MRI lesion maps were segmented on diffusion-weighted images, spatially normalized, and the extent of overlap between the different stroke patterns was determined. Correlations between the period with abnormal yawning and the apparent diffusion coefficient (ADC) in the overlapping regions, total stroke volume, NIHSS and mRS were performed. Periods in which patients presented with episodes of abnormal yawning lasted on average for 58 h. Average GCS, NIHSS, and mRS scores were 12.6, 11.6, and 3.5, respectively. Clinical parameters were within normal limits. Ischemic brain lesions overlapped in nine out of ten patients: in seven patients in the insula and in seven in the caudate nucleus. The decrease of the ADC within the lesions correlated with the period with abnormal yawing (r = -0.76, Bonferroni-corrected p = 0.02). The stroke lesion intensity of the common overlapping regions in the insula and the caudate nucleus correlates with the period with abnormal yawning. The insula might be the long sought-after brain region for serotonin-mediated yawning.

In schizophrenia there is a consistent epidemiological finding of a birth excess in winter and spring. Season of birth is thought to act as a proxy indicator for harmful environmental factors during foetal maturation. There is evidence that prenatal exposure to harmful environmental factors may trigger pathologic processes in the neurodevelopment, which subsequently increase the risk of schizophrenia. Since brain white matter alterations have repeatedly been found in schizophrenia, the objective of this study was to investigate whether white matter integrity was related to the season of birth in patients with schizophrenia. Thirty-four patients with schizophrenia and 33 healthy controls underwent diffusion tensor imaging. Differences in the fractional anisotropy maps of schizophrenia patients and healthy controls born in different seasons were analysed with tract-based spatial statistics. A significant main effect of season of birth and an interaction of group and season of birth showed that patients born in summer had significantly lower fractional anisotropy in widespread white matter regions than those born in the remainder of the year. Additionally, later age of schizophrenia onset was found in patients born in winter months. The current findings indicate a relationship of season of birth and white matter alterations in schizophrenia and consequently support the neurodevelopmental hypothesis of early pathological mechanisms in schizophrenia.