The Human Cerebellum: A Digital Anatomical Atlas at the Level of Individual Folia.

Scientific interest in the cerebellum has surged in the last few decades with an emerging consensus on a multifaceted functionality and intricate, but not yet fully understood, functional topography over the cerebellar cortex. To further refine this structure-function relationship and quantify its inter-subject variability, a high-resolution digital anatomical atlas is fundamental. Using a combination of manual labeling and image processing, we turned a recently published reconstruction of the human cerebellum, the first such reconstruction fine enough to resolve the individual folia, into a digital atlas with both surface and volumetric representations. Its unprecedented granularity (0.16 mm) and detailed expert labeling make the atlas usable as an anatomical ground truth, enabling new ways of analyzing and visualizing cerebellar data through its digital format.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Individualized Functional Brain System Topologies and Major Depression: Relationships Among Patch Sizes and Clinical Profiles and Behavior.

BACKGROUND: Neuroimaging studies of major depression have typically been conducted using group-level approaches. However, given interindividual differences in brain systems, there is a need for individualized approaches to brain systems mapping and putative links toward diagnosis, symptoms, and behavior. METHODS: We used an iterative parcellation approach to map individualized brain systems in 328 participants from a multisite, placebo-controlled clinical trial. We hypothesized that participants with depression would show abnormalities in salience, control, default, and affective systems, which would be associated with higher levels of self-reported anhedonia, anxious arousal, and worse cognitive performance. Within hypothesized brain systems, we compared patch sizes (number of vertices) between depressed and healthy control groups. Within depressed groups, abnormal patches were correlated with hypothesized clinical and behavioral measures. RESULTS: Significant group differences emerged in hypothesized patches of 1) the lateral salience system (parietal operculum; t326 = -3.11, p = .002) and 2) the control system (left medial posterior prefrontal cortex region; z = -3.63, p < .001), with significantly smaller patches in these regions in participants with depression than in healthy control participants. Results suggest that participants with depression with significantly smaller patch sizes in the lateral salience system and control system regions experience greater anxious arousal and cognitive deficits. CONCLUSIONS: The findings imply that neural features mapped at the individual level may relate meaningfully to diagnosis, symptoms, and behavior. There is strong clinical relevance in taking an individualized brain systems approach to mapping neural functional connectivity because these associated region patch sizes may help advance our understanding of neural features linked to psychopathology and foster future patient-specific clinical decision making.

CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.

Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).

Unravelling the impact of frontal lobe impairment for social dysfunction in myotonic dystrophy type 1.

Myotonic dystrophy type 1 is an autosomal dominant multisystemic disorder affecting muscular and extra muscular systems, including the central nervous system. Cerebral involvement in myotonic dystrophy type 1 is associated with subtle cognitive and behavioural disorders, of major impact on socio-professional adaptation. The social dysfunction and its potential relation to frontal lobe neuropsychology remain under-evaluated in this pathology. The neuroanatomical network underpinning that disorder is yet to disentangle. Twenty-eight myotonic dystrophy type 1 adult patients (mean age: 46 years old) and 18 age and sex-matched healthy controls were included in the study. All patients performed an exhaustive neuropsychological assessment with a specific focus on frontal lobe neuropsychology (motivation, social cognition and executive functions). Among them, 18 myotonic dystrophy type 1 patients and 18 healthy controls had a brain MRI with T1 and T2 Flair sequences. Grey matter segmentation, Voxel-based morphometry and cortical thickness estimation were performed with Statistical Parametric Mapping Software SPM12 and Freesurfer software. Furthermore, T2 white matter lesions and subcortical structures were segmented with Automated Volumetry Software. Most patients showed significant impairment in executive frontal functions (auditory working memory, inhibition, contextualization and mental flexibility). Patients showed only minor difficulties in social cognition tests mostly in cognitive Theory of Mind, but with relative sparing of affective Theory of Mind and emotion recognition. Neuroimaging analysis revealed atrophy mostly in the parahippocampal and hippocampal regions and to a lesser extent in basal ganglia, regions involved in social navigation and mental flexibility, respectively. Social cognition scores were correlated with right parahippocampal gyrus atrophy. Social dysfunction in myotonic dystrophy type 1 might be a consequence of cognitive impairment regarding mental flexibility and social contextualization rather than a specific social cognition deficit such as emotion recognition. We suggest that both white matter lesions and grey matter disease could account for this social dysfunction, involving, in particular, the frontal-subcortical network and the hippocampal/arahippocampal regions, brain regions known, respectively, to integrate contextualization and social navigation.

FastSurferVINN: Building resolution-independence into deep learning segmentation methods-A solution for HighRes brain MRI.

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm for improved structure definition and morphometry. Yet, only few, time-intensive automated image analysis pipelines have been validated for high-resolution (HiRes) settings. Efficient deep learning approaches, on the other hand, rarely support more than one fixed resolution (usually 1.0 mm). Furthermore, the lack of a standard submillimeter resolution as well as limited availability of diverse HiRes data with sufficient coverage of scanner, age, diseases, or genetic variance poses additional, unsolved challenges for training HiRes networks. Incorporating resolution-independence into deep learning-based segmentation, i.e., the ability to segment images at their native resolution across a range of different voxel sizes, promises to overcome these challenges, yet no such approach currently exists. We now fill this gap by introducing a Voxel-size Independent Neural Network (VINN) for resolution-independent segmentation tasks and present FastSurferVINN, which (i) establishes and implements resolution-independence for deep learning as the first method simultaneously supporting 0.7-1.0 mm whole brain segmentation, (ii) significantly outperforms state-of-the-art methods across resolutions, and (iii) mitigates the data imbalance problem present in HiRes datasets. Overall, internal resolution-independence mutually benefits both HiRes and 1.0 mm MRI segmentation. With our rigorously validated FastSurferVINN we distribute a rapid tool for morphometric neuroimage analysis. The VINN architecture, furthermore, represents an efficient resolution-independent segmentation method for wider application.

Examining volumetric gradients based on the frustum surface ratio in the brain in autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is accompanied by neurodevelopmental differences in regional cortical volume (CV), and a potential layer-specific pathology. Conventional measures of CV, however, do not indicate how volume is distributed across cortical layers. In a sample of 92 typically developing (TD) controls and 92 adult individuals with ASD (aged 18-52 years), we examined volumetric gradients by quantifying the degree to which CV is weighted from the pial to the white surface of the brain. Overall, the spatial distribution of Frustum Surface Ratio (FSR) followed the gyral and sulcal pattern of the cortex and approximated a bimodal Gaussian distribution caused by a linear mixture of vertices on gyri and sulci. Measures of FSR were highly correlated with vertex-wise estimates of mean curvature, sulcal depth, and pial surface area, although none of these features explained more than 76% variability in FSR on their own. Moreover, in ASD, we observed a pattern of predominant increases in the degree of FSR relative to TD controls, with an atypical neurodevelopmental trajectory. Our findings suggest a more outward-weighted gradient of CV in ASD, which may indicate a larger contribution of supragranular layers to regional differences in CV.

FastSurfer - A fast and accurate deep learning based neuroimaging pipeline.

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 â€‹min) and surface-based thickness analysis (within only around 1 â€‹h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia.

The Importance of Considering Model Choices When Interpreting Results in Computational Neuroimaging.

Model-based analyses open exciting opportunities for understanding neural information processing. In a commentary published in eNeuro, Gardner and Liu (2019) discuss the role of model specification in interpreting results derived from complex models of neural data. As a case study, they suggest that one such analysis, the inverted encoding model (IEM), should not be used to assay properties of "stimulus representations" because the ability to apply linear transformations at various stages of the analysis procedure renders results "arbitrary." Here, we argue that the specification of all models is arbitrary to the extent that an experimenter makes choices based on current knowledge of the model system. However, the results derived from any given model, such as the reconstructed channel response profiles obtained from an IEM analysis, are uniquely defined and are arbitrary only in the sense that changes in the model can predictably change results. IEM-based channel response profiles should therefore not be considered arbitrary when the model is clearly specified and guided by our best understanding of neural population representations in the brain regions being analyzed. Intuitions derived from this case study are important to consider when interpreting results from all model-based analyses, which are similarly contingent upon the specification of the models used.

Dissociable signatures of visual salience and behavioral relevance across attentional priority maps in human cortex.

Computational models posit that visual attention is guided by activity within spatial maps that index the image-computable salience and the behavioral relevance of objects in the scene. These spatial maps are theorized to be instantiated as activation patterns across a series of retinotopic visual regions in occipital, parietal, and frontal cortex. Whereas previous research has identified sensitivity to either the behavioral relevance or the image-computable salience of different scene elements, the simultaneous influence of these factors on neural "attentional priority maps" in human cortex is not well understood. We tested the hypothesis that visual salience and behavioral relevance independently impact the activation profile across retinotopically organized cortical regions by quantifying attentional priority maps measured in human brains using functional MRI while participants attended one of two differentially salient stimuli. We found that the topography of activation in priority maps, as reflected in the modulation of region-level patterns of population activity, independently indexed the physical salience and behavioral relevance of each scene element. Moreover, salience strongly impacted activation patterns in early visual areas, whereas later visual areas were dominated by relevance. This suggests that prioritizing spatial locations relies on distributed neural codes containing graded representations of salience and relevance across the visual hierarchy. NEW & NOTEWORTHY We tested a theory which supposes that neural systems represent scene elements according to both their salience and their relevance in a series of "priority maps" by measuring functional MRI activation patterns across human brains and reconstructing spatial maps of the visual scene. We found that different regions indexed either the salience or the relevance of scene items, but not their interaction, suggesting an evolving representation of salience and relevance across different visual areas.

Orientation decoding: Sense in spirals?

The orientation of a visual stimulus can be successfully decoded from the multivariate pattern of fMRI activity in human visual cortex. Whether this capacity requires coarse-scale orientation biases is controversial. We and others have advocated the use of spiral stimuli to eliminate a potential coarse-scale bias-the radial bias toward local orientations that are collinear with the centre of gaze-and hence narrow down the potential coarse-scale biases that could contribute to orientation decoding. The usefulness of this strategy is challenged by the computational simulations of Carlson (2014), who reported the ability to successfully decode spirals of opposite sense (opening clockwise or counter-clockwise) from the pooled output of purportedly unbiased orientation filters. Here, we elaborate the mathematical relationship between spirals of opposite sense to confirm that they cannot be discriminated on the basis of the pooled output of unbiased or radially biased orientation filters. We then demonstrate that Carlson's (2014) reported decoding ability is consistent with the presence of inadvertent biases in the set of orientation filters; biases introduced by their digital implementation and unrelated to the brain's processing of orientation. These analyses demonstrate that spirals must be processed with an orientation bias other than the radial bias for successful decoding of spiral sense.