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Children often show cognitive and affective traits that are similar to their parents. Although this indicates a transmission of phenotypes from parents to children, little is known about the neural underpinnings of that transmission. Here, we provide a general overview of neuroimaging studies that explore the similarity between parents and children in terms of brain structure and function. We notably discuss the aims, designs, and methods of these so-called intergenerational neuroimaging studies, focusing on two main designs: the parent-child design and the multigenerational design. For each design, we also summarize the major findings, identify the sources of variability between studies, and highlight some limitations and future directions. We argue that the lack of consensus in defining the parent-child transmission of brain structure and function leads to measurement heterogeneity, which is a challenge for future studies. Additionally, multigenerational studies often use measures of family resemblance to estimate the proportion of variance attributed to genetic versus environmental factors, though this estimate is likely inflated given the frequent lack of control for shared environment. Nonetheless, intergenerational neuroimaging studies may still have both clinical and theoretical relevance, not because they currently inform about the etiology of neuromarkers, but rather because they may help identify neuromarkers and test hypotheses about neuromarkers coming from more standard neuroimaging designs. Highlights: We review neuroimaging studies investigating neural markers of traits transmission. Studies have used both parent-child and multigenerational designs. Studies provide insights but suffer from lack of methodological standardization. Multigenerational studies should also account for shared environment. [ABSTRACT FROM AUTHOR]

Modern voxel-based lesion-symptom mapping (VLSM) analyses techniques provide powerful tools to examine the relationship between structure and function of the healthy human brain. However, there is still uncertainty on the type of and the appropriate time point of imaging and of behavioral testing for such analyses. Here we tested the validity of the three most common combinations of structural imaging data and behavioral scores used in VLSM analyses. Given the established knowledge about the neural substrate of the primary motor system in humans, we asked the mundane question of where the motor system is represented in the normal human brain, analyzing individual arm motor function of 60 unselected stroke patients. Only the combination of acute behavioral scores and acute structural imaging precisely identified the principal brain area for the emergence of hemiparesis after stroke, i.e., the corticospinal tract (CST). In contrast, VLSM analyses based on chronic behavior-in combination with either chronic or acute imaging-required the exclusion of patients who had recovered from an initial paresis to reveal valid anatomical results. Thus, if the primary research aim of a VLSM lesion analysis is to uncover the neural substrates of a certain function in the healthy human brain and if no longitudinal designs with repeated evaluations are planned, the combination of acute imaging and behavior represents the ideal dataset. [ABSTRACT FROM AUTHOR]

The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure-function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples ( N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database. [ABSTRACT FROM AUTHOR]

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering. [ABSTRACT FROM AUTHOR]

The left hemisphere lateralization bias for language functions, such as syntactic processing and semantic retrieval, is well known. Although several theories and clinical data indicate a link between speech motor execution and language, the functional and structural brain lateralization for these functions has never been examined concomitantly in the same individuals. Here, we used functional MRI during rapid silent syllable repetition (/lalala/, /papapa/ and /pataka/, known as oral diadochokinesis or DDK) to map the cortical representation of the articulators in 17 healthy adults. In these same participants, functional lateralization for language production was assessed using the well-established verb generation task. We then used DDK-related fMRI activation clusters to guide tractography of the corticobulbar tract from diffusion-weighted MRI. Functional MRI revealed a wide inter-individual variability of hemispheric asymmetry patterns (left and right dominant, as well as bilateral) for DDK in the motor cortex, despite predominantly left hemisphere dominance for language-related activity in Broca's area. Tractography revealed no evidence for structural asymmetry (based on fractional anisotropy) within the corticobulbar tract. To our knowledge, this study is the first to reveal that motor brain activation for syllable repetition is unrelated to functional asymmetry for language production in adult humans. In addition, we found no evidence that the human corticobulbar tract is an asymmetric white matter pathway. We suggest that the predominance of dysarthria following left hemisphere infarct is probably a consequence of disrupted feedback or input from left hemisphere language and speech planning regions, rather than structural asymmetry of the corticobulbar tract itself. [ABSTRACT FROM AUTHOR]

Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neuroimaging data, but a limited small number of samples available, makes it challenging to build a robust computer-aided diagnosis system. Machine learning techniques have been considered as a useful tool in this respect and, among various methods, sparse regression has shown its validity in the literature. However, to our best knowledge, the existing sparse regression methods mostly try to select features based on the optimal regression coefficients in one step. We argue that since the training feature vectors are composed of both informative and uninformative or less informative features, the resulting optimal regression coefficients are inevidently affected by the uninformative or less informative features. To this end, we first propose a novel deep architecture to recursively discard uninformative features by performing sparse multi-task learning in a hierarchical fashion. We further hypothesize that the optimal regression coefficients reflect the relative importance of features in representing the target response variables. In this regard, we use the optimal regression coefficients learned in one hierarchy as feature weighting factors in the following hierarchy, and formulate a weighted sparse multi-task learning method. Lastly, we also take into account the distributional characteristics of samples per class and use clustering-induced subclass label vectors as target response values in our sparse regression model. In our experiments on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI diagnosis and showed the superiority of the proposed method by comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The increasing understanding of human brain functions makes it possible to directly interact with the brain for therapeutic purposes. Implantable brain computer interfaces promise to replace or restore motor functions in patients with partial or complete paralysis. We postulate that neuronal states associated with gestures, as they are used in the finger spelling alphabet of sign languages, provide an excellent signal for implantable brain computer interfaces to restore communication. To test this, we evaluated decodability of four gestures using high-density electrocorticography in two participants. The electrode grids were located subdurally on the hand knob area of the sensorimotor cortex covering a surface of 2.5-5.2 cm. Using a pattern-matching classification approach four types of hand gestures were classified based on their pattern of neuronal activity. In the two participants the gestures were classified with 97 and 74 % accuracy. The high frequencies (>65 Hz) allowed for the best classification results. This proof-of-principle study indicates that the four gestures are associated with a reliable and discriminable spatial representation on a confined area of the sensorimotor cortex. This robust representation on a small area makes hand gestures an interesting control feature for an implantable BCI to restore communication for severely paralyzed people. [ABSTRACT FROM AUTHOR]

The mammalian habenula with its medial and lateral complexes has gained much interest in recent years, while knowledge on the detailed biological functions of these nuclei is still scarce. Novel strategies to differentiate and identify habenular cell types are required. Such attempts have largely failed, most likely due to the lack of appropriate molecular markers. One important tool to approach this dilemma is available in form of the Allen Brain Atlas (ABA), which provides detailed expression patterns of many genes in the mouse brain. In the present report, ABA tools in combination with visual inspection of ISH images were used to detect transcripts, which are strongly expressed in medial (MHb) and lateral (LHb) habenular complexes. Against our expectations, most transcripts were differentially distributed throughout the LHb, disregarding boundaries of subnuclear areas. Nine distinct distribution patterns were recognized. Yet, several transcripts could not be attributed to one of these, suggesting a high diversity of neuron types in the LHb. In the MHb, in contrast, many transcripts tended to obey subnuclear boundaries. The differential distribution of others like Adcyap1, Chrna3, or Trp53i11 suggests the presence of a novel subfield adjacent to the region of the MHbVm, which now is termed intermediate field of the ventral MHb. In addition, the localizations of Amigo2, Adcyap1, and a couple of other transcripts suggest a lateral extension of the MHb, which is here, termed HbX area. Apparently, this area is composed of intermingled MHb and LHb neurons and may allow functional interaction between the both habenular complexes. [ABSTRACT FROM AUTHOR]

Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation. [ABSTRACT FROM AUTHOR]

Murine sex chromosome aneuploidies (SCAs) provide powerful models for charting sex chromosome influences on mammalian brain development. Here, building on prior work in X-monosomic (XO) mice, we use spatially non-biased high-resolution imaging to compare and contrast neuroanatomical alterations in XXY and XO mice relative to their wild-type XX and XY littermates. First, we show that carriage of a supernumerary X chromosome in XXY males (1) does not prevent normative volumetric masculinization of the bed nucleus of the stria terminalis (BNST) and medial amygdala, but (2) causes distributed anatomical alterations relative to XY males, which show a statistically unexpected tendency to be co-localized with and reciprocal to XO-XX differences in anatomy. These overlaps identify the lateral septum, BNST, ventral group thalamic nuclei and periaqueductal gray matter as regions with replicable sensitivity to X chromosome dose across two SCAs. We then harness anatomical variation across all four karyotype groups in our study-XO, XX, XY and XXY-to create an agnostic data-driven segmentation of the mouse brain into five distributed clusters which (1) recover fundamental properties of brain organization with high spatial precision, (2) define two previously uncharacterized systems of relative volume excess in females vs. males ('forebrain cholinergic' and 'cerebelo-pontine-thalamo-cortical'), and (3) adopt stereotyped spatial motifs which delineate ordered gradients of sex chromosome and gonadal influences on volumetric brain development. Taken together, these data provide a new framework for the study of sexually dimorphic influences on brain development in health and disrupted brain development in SCA. [ABSTRACT FROM AUTHOR]

To evaluate brain development longitudinally in premature infants without abnormalities as compared to healthy full-term newborns, we assessed fMRI brain activity patterns in response to linguistic stimuli and white matter structural development focusing on language-related fibres. A total sample of 29 preterm newborns and 26 at term control newborns underwent both fMRI and DTI. Griffiths test was performed at 6 months of corrected age to assess development. Auditory fMRI data were analysed in 17 preterm newborns at three time points [34, 41 and 44 weeks of post menstrual age (wPMA)] and in 15 controls, at term. Analysis showed a distinctive pattern of cortical activation in preterm newborns up to 29 wPMA moving from early prevalent left temporal and supramarginal area activation in the preterm period, to a bilateral temporal and frontoopercular activation in the at term equivalent period and to a more fine-grained left pattern of activity at 44 wPMA. At term controls showed instead greater bilateral posterior thalamic activation. The different pattern of brain activity associated to preterm newborns mirrors their white matter maturation delay in peripheral regions of the fibres and thalamo-cortical radiations in subcortical areas of both hemispheres, pointing to different transient thalamo-cortical development due to prematurity. Evidence for functional thalamic activation and more mature subcortical tracts, including thalamic radiations, may represent the substantial gap between preterm and at term infants. The transition between bilateral temporal activations at term age and leftward activations at 44 weeks of PMA is correlated to better neuropsychological results in Griffiths test. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the structure, function and behavior of the human brain. It mentions that neuroscience has the history in studying the anatomy of the brain with studies of grey matter and white matter fibers as technology developed, however, neuroimaging led to the neglect of brain structure. Moreover, brain anatomy and brain function are linked and together determine behavioral responses.