Neuroimaging studies in autism spectrum disorders (ASDs) have provided inconsistent evidence of cortical abnormality. This is probably due to the small sample sizes used in most studies, and ...important differences in sample characteristics, particularly age, as well as to the heterogeneity of the disorder. To address these issues, we assessed abnormalities in ASD within the Autism Brain Imaging Data Exchange data set, which comprises data from approximately 1100 individuals (~6-55 years). A subset of these data that met stringent quality control and inclusion criteria (560 male subjects; 266 ASD; age = 6-35 years) were used to compute age-specific differences in cortical thickness in ASD and the relationship of any such differences to symptom severity of ASD. Our results show widespread increased cortical thickness in ASD, primarily left lateralized, from 6 years onwards, with differences diminishing during adulthood. The severity of symptoms related to social affect and communication correlated with these cortical abnormalities. These results are consistent with the conjecture that developmental patterns of cortical thickness abnormalities reflect delayed cortical maturation and highlight the dynamic nature of morphological abnormalities in ASD.
There is evidence from the visual, verbal, and tactile memory domains that the midventrolateral prefrontal cortex plays a critical role in the top–down modulation of activity within posterior ...cortical areas for the selective retrieval of specific aspects of a memorized experience, a functional process often referred to as active controlled retrieval. In the present functional neuroimaging study, we explore the neural bases of active retrieval for auditory nonverbal information, about which almost nothing is known. Human participants were scanned with functional magnetic resonance imaging (fMRI) in a task in which they were presented with short melodies from different locations in a simulated virtual acoustic environment within the scanner and were then instructed to retrieve selectively either the particular melody presented or its location. There were significant activity increases specifically within the midventrolateral prefrontal region during the selective retrieval of nonverbal auditory information. During the selective retrieval of information from auditory memory, the right midventrolateral prefrontal region increased its interaction with the auditory temporal region and the inferior parietal lobule in the right hemisphere. These findings provide evidence that the midventrolateral prefrontal cortical region interacts with specific posterior cortical areas in the human cerebral cortex for the selective retrieval of object and location features of an auditory memory experience.
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Accurate automated quantification of subcortical structures is a greatly pursued endeavour in neuroimaging. In an effort to establish the validity and reliability of these methods in defining the ...striatum, globus pallidus, and thalamus, we investigated differences in volumetry between manual delineation and automated segmentations derived by widely used FreeSurfer and FSL packages, and a more recent segmentation method, the MAGeT-Brain algorithm. In a first set of experiments, the basal ganglia and thalamus of thirty subjects (15 first episode psychosis FEP, 15 controls) were manually defined and compared to the labels generated by the three automated methods. Our results suggest that all methods overestimate volumes compared to the manually derived “gold standard”, with the least pronounced differences produced using MAGeT. The least between-method variability was noted for the striatum, whereas marked differences between manual segmentation and MAGeT compared to FreeSurfer and FSL emerged for the globus pallidus and thalamus. Correlations between manual segmentation and automated methods were strongest for MAGeT (range: 0.51 to 0.92; p<0.01, corrected), whereas FreeSurfer and FSL showed moderate to strong Pearson correlations (range 0.44–0.86; p<0.05, corrected), with the exception of FreeSurfer pallidal (r=0.31, p=0.10) and FSL thalamic segmentations (r=0.37, p=0.051). Bland-Altman plots highlighted a tendency for greater volumetric differences between manual labels and automated methods at the lower end of the distribution (i.e. smaller structures), which was most prominent for bilateral thalamus across automated pipelines, and left globus pallidus for FSL.
We then went on to examine volume and shape of the basal ganglia structures using automated techniques in 135 FEP patients and 88 controls. The striatum and globus pallidus were significantly larger in FEP patients compared to controls bilaterally, irrespective of the method used. MAGeT-Brain was more sensitive to shape-based group differences, and uncovered widespread surface expansions in the striatum and globus pallidus bilaterally in FEP patients compared to controls, and surface contractions in bilateral thalamus (FDR-corrected). By contrast, after using a recommended cluster-wise thresholding method, FSL only detected differences in the right ventral striatum (FEP>Control) and one cluster of the left thalamus (Control>FEP).
These results suggest that different automated pipelines segment subcortical structures with varying degrees of variability compared to manual methods, with particularly pronounced differences found with FreeSurfer and FSL for the globus pallidus and thalamus.
•Manual segmentation of subcortical structure is evaluated against 3 automated tools.•Correspondence with manual labels was greatest with MAGeT-Brain.•Greatest between-method variability was noted for FreeSurfer and FSL-FIRST.•Larger striatum and pallidum were found in first episode psychosis across methods.•Subcortical shape profiles in patients vs. controls differed between MAGeT and FSL.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A confusing picture of the functional organization of the dorsal premotor region of the human brain emerged when functional neuroimaging studies that either examined visuomotor hand conditional ...activity or attempted to localize the human frontal eye field reported activity increases at the same general location, namely the junction of the superior precentral sulcus with the superior frontal sulcus. The present functional magnetic resonance imaging study examined visuomotor hand conditional activity and the locus of the frontal eye field as defined by a standard task, on a subject-by-subject basis, to clarify their location and reveal relationships between the pattern of local morphology and functional activity. The results demonstrate that visuomotor hand conditional activity and the frontal eye field lie within distinct parts of the superior precentral sulcus, revealing an organization of the human premotor cortex consistent with that observed in experimental studies in the monkey.
Neuroimaging has been facing a data deluge characterized by the exponential growth of both raw and processed data. As a result, mining the massive quantities of digital data collected in these ...studies offers unprecedented opportunities and has become paramount for today's research. As the neuroimaging community enters the world of “Big Data”, there has been a concerted push for enhanced sharing initiatives, whether within a multisite study, across studies, or federated and shared publicly. This article will focus on the database and processing ecosystem developed at the Montreal Neurological Institute (MNI) to support multicenter data acquisition both nationally and internationally, create database repositories, facilitate data-sharing initiatives, and leverage existing software toolkits for large-scale data processing.
•We outline the MNI data-sharing ecosystem, which include the LORIS and CBRAIN platforms.•We detail what tools and pipelines these platforms use (e.g., CIVET, MINC, FSL).•A step-by-step delineation of the MNI ecosystem is given from data acquisition to dissemination.•Five examples of public data-sharing repositories from the MNI ecosystem services are outlined.•We discuss a number of important data-sharing challenges and considerations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Tactile sensory information is first channeled from the primary somatosensory cortex on the postcentral gyrus to the parietal opercular region (i.e., the secondary somatosensory cortex) and the ...rostral inferior parietal lobule and, from there, to the prefrontal cortex, with which bidirectional connections exist. Although we know that tactile memory signals can be found in the prefrontal cortex, the contribution of the different prefrontal areas to tactile memory remains unclear. The present functional MRI study shows that a specific part of the prefrontal cortex in the human brain, namely the midventrolateral prefrontal region (cytoarchitectonic areas 47/12 and 45), is involved in active controlled retrieval processing necessary for the disambiguation of vibrotactile information in short-term memory. Furthermore, we demonstrate that this particular part of the prefrontal cortex interacts functionally with the secondary somatosensory areas in the parietal operculum and the rostral inferior parietal lobule during controlled processing for the retrieval of specific tactile information.
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Having recently demonstrated that the human orbitofrontal cortex is selectively activated during the encoding of visual information, we investigated whether this same frontal region, which is ...directly connected to medial temporal structures, would be activated during the encoding of auditory stimuli. We measured cerebral blood flow (CBF) with positron emission tomography (PET) during the encoding of nonverbal abstract auditory stimuli in a group of young healthy volunteers. The results demonstrate that the left orbitofrontal cortex, area 11 in particular, is involved in the encoding of auditory information. We suggest that the orbitofrontal cortex is a critical frontal region that can exert top-down regulation of other regions of the brain including the medial temporal structures and the lateral frontal cortex, enabling the further processing of information.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Children who developed autism spectrum disorders (ASDs) by age 2 had greater development of cerebral white matter fiber tracts by 6 months than unaffected children. After the initial accelerated ...white matter development, the children who developed ASDs had slower development, so that by age 2 their white matter development was less than that in the unaffected children.
Objective:Evidence from prospective studies of high-risk infants suggests that early symptoms of autism usually emerge late in the first or early in the second year of life after a period of relatively typical development. The authors prospectively examined white matter fiber tract organization from 6 to 24 months in high-risk infants who developed autism spectrum disorders (ASDs) by 24 months.
Method:The participants were 92 high-risk infant siblings from an ongoing imaging study of autism. All participants had diffusion tensor imaging at 6 months and behavioral assessments at 24 months; a majority contributed additional imaging data at 12 and/or 24 months. At 24 months, 28 infants met criteria for ASDs and 64 infants did not. Microstructural properties of white matter fiber tracts reported to be associated with ASDs or related behaviors were characterized by fractional anisotropy and radial and axial diffusivity.
Results:The fractional anisotropy trajectories for 12 of 15 fiber tracts differed significantly between the infants who developed ASDs and those who did not. Development for most fiber tracts in the infants with ASDs was characterized by higher fractional anisotropy values at 6 months followed by slower change over time relative to infants without ASDs. Thus, by 24 months of age, those with ASDs had lower values.
Conclusions:These results suggest that aberrant development of white matter pathways may precede the manifestation of autistic symptoms in the first year of life. Longitudinal data are critical to characterizing the dynamic age-related brain and behavior changes underlying this neurodevelopmental disorder.
There is a growing body of evidence indicating that the mid‐ventrolateral prefrontal cortex in the left hemisphere is involved in some aspect of controlled verbal memory retrieval. Its precise role, ...however, remains unclear. We tested the hypothesis that when stimuli in memory are related to each other in multiple ways, and therefore familiarity, strong constant stimulus–stimulus links or contextual cues are not sufficient for successful retrieval, control processing emanating from the mid‐ventrolateral prefrontal cortex is required to disambiguate and select the appropriate information among memory traces. We refer to this type of retrieval as active retrieval to distinguish it from automatic retrieval which depends on the simple reactivation of memory traces. Normal human subjects were scanned with functional magnetic resonance imaging while they performed three memory tasks that varied in their demands on active retrieval of verbal information. As the demands on active retrieval increased, there was an increase in the activity within the mid‐ventrolateral prefrontal cortex, bilaterally, but with more prominent activity in the left hemisphere. These activity increases correlated with activity in the posterior temporal region which, in the left hemisphere, is involved in language processing. No significant activity increases were observed in any other prefrontal region. Furthermore, for the retrieval of well‐learned verbally cued conditional motor associations, there were no activity increases in the mid‐ventrolateral prefrontal cortex. The present findings provide strong support for the hypothesis that the mid‐ventrolateral prefrontal cortex, particularly in the left hemisphere, plays a major role in the active retrieval of information from verbal memory.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
To delineate the early progression of autism spectrum disorder (ASD) symptoms, this study investigated developmental characteristics of infants at high familial risk for ASD (HR), and infants at low ...risk (LR).
Participants included 210 HR and 98 LR infants across 4 sites with comparable behavioral data at age 6, 12, and 24 months assessed in the domains of cognitive development (Mullen Scales of Early Learning), adaptive skills (Vineland Adaptive Behavioral Scales), and early behavioral features of ASD (Autism Observation Scale for Infants). Participants evaluated according to the DSM-IV-TR criteria at 24 months and categorized as ASD-positive or ASD-negative were further stratified by empirically derived cutoff scores using the Autism Diagnostic Observation Schedule yielding four groups: HR-ASD-High, HR-ASD-Moderate (HR-ASD-Mod), HR-ASD-Negative (HR-Neg), and LR-ASD-Negative (LR-Neg).
The four groups demonstrated different developmental trajectories that became increasingly distinct from 6 to 24 months across all domains. At 6 months, the HR-ASD-High group demonstrated less advanced Gross Motor and Visual Reception skills compared with the LR-Neg group. By 12 months, the HR-ASD-High group demonstrated increased behavioral features of ASD and decreased cognitive and adaptive functioning compared to the HR-Neg and LR-Neg groups. By 24 months, both the HR-ASD-High and HR-ASD-Moderate groups demonstrated differences from the LR- and HR-Neg groups in all domains.
These findings reveal atypical sensorimotor development at 6 months of age which is associated with ASD at 24 months in the most severely affected group of infants. Sensorimotor differences precede the unfolding of cognitive and adaptive deficits and behavioral features of autism across the 6- to 24-month interval. The less severely affected group demonstrates later symptom onset, in the second year of life, with initial differences in the social-communication domain.
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