Quantitative temporal lobe differences: autism distinguished from controls using classification and regression tree analysis

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8-12 years old with ASD were matched with 105 children aged 8-12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.

Individual brain regulation as learned via neurofeedback is related to affective changes in adolescents with autism spectrum disorder

BACKGROUND

Emotions often play a role in neurofeedback (NF) regulation strategies. However, investigations of the relationship between the induced neuronal changes and improvements in affective domains are scarce in electroencephalography-based studies. Thus, we extended the findings of the first study on slow cortical potential (SCP) NF in autism spectrum disorder (ASD) by linking affective changes to whole-brain activity during rest and regulation.

METHODS

Forty-one male adolescents with ASD were scanned twice at rest using functional magnetic resonance imaging. Between scans, half underwent NF training, whereas the other half received treatment as usual. Furthermore, parents reported on their child's affective characteristics at each measurement. The NF group had to alternatingly produce negative and positive SCP shifts during training and was additionally scanned using functional magnetic resonance imaging while applying their developed regulation strategies.

RESULTS

No significant treatment group-by-time interactions in affective or resting-state measures were found. However, we found increases of resting activity in the anterior cingulate cortex and right inferior temporal gyrus as well as improvements in affective characteristics over both groups. Activation corresponding to SCP differentiation in these regions correlated with the affective improvements. A further correlation was found for Rolandic operculum activation corresponding to positive SCP shifts. There were no significant correlations with the respective achieved SCP regulation during NF training.

CONCLUSION

SCP NF in ASD did not lead to superior improvements in neuronal or affective functioning compared to treatment as usual. However, the affective changes might be related to the individual strategies and their corresponding activation patterns as indicated by significant correlations on the whole-brain level. Trial registration This clinical trial was registered at drks.de (DRKS00012339) on 20th April, 2017.

Super responders: Predicting language gains from JASPER among limited language children with autism spectrum disorder

Early intervention can provide a great benefit for children with autism spectrum disorder (ASD). However, no single intervention is effective for all children. Even when an intervention is effective overall, individual child response varies. Some children make incredible progress, and others make slow or no progress. Therefore, it is important that the field move towards developing methods to personalize intervention. Operationalizing meaningful change and predicting intervention response are critical steps in designing systematic and personalized early intervention. The present research used improvement in expressive language to group children that received a targeted social communication early intervention, Joint Attention, Symbolic Play, Engagement, and Regulation (JASPER), into super responders and slow responders. Using baseline data from traditional standardized assessments of cognition and behavioral data from validated experimental measures of play and social communication, we used conditional inference tree models to predict responder status. From a sample of 99 preschool age, limited language children with ASD, play diversity was the most significant predictor of responder status. Children that played functionally with a wider variety of toys had increased odds of being a super responder to JASPER. A combination of lower play diversity and impairments in fine motor abilities increased the odds of children being slow responders to JASPER. Results from the present study can inform future efforts to individualize intervention and systematic approaches to augmenting treatment in real time. LAY SUMMARY: To help us answer the question of for whom an intervention works best, we examined 99 children, age three to five, who qualified as being limited spoken language communicators, and received a targeted intervention for social communication and language. We used child characteristics before intervention to predict which children would improve their language the most and found that the ability to play appropriately with a wider variety of toys predicted the best improvements in expressive language. These findings will help better inform future work to individualize intervention based on the unique needs of each child.

Victory Prediction of Ladies Professional Golf Association Players: Influential Factors and Comparison of Prediction Models

This study aims to identify the most accurate prediction model for the possibility of victory from the annual average data of 25 seasons (1993–2017) of the Ladies Professional Golf Association (LPGA), and to determine the importance of the predicting factors. The four prediction models considered in this study were a decision tree, discriminant analysis, logistic regression, and artificial neural network analysis. The mean difference in the classification accuracy of these models was analyzed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA) and the one-way analysis of variance (ANOVA). When the prediction was based on technical variables, the most important predicting variables for determining victory were greens in regulation (GIR) and putting average (PA) in all four prediction models. When the prediction was based on the output of the technical variables, the most important predicting variable for determining victory was birdies in all four prediction models. When the prediction was based on the season outcome, the most important predicting variables for determining victory were the top 10 finish% (T10) and official money. A significant mean difference in classification accuracy was observed while performing the one-way ANOVA, and the least significant difference post-hoc test showed that artificial neural network analysis exhibited higher accuracy than the other models, especially, for larger data sizes. From the results of this study, it can be inferred that the player who wants to win the LPGA should aim to increase GIR, reduce PA, and improve driving distance and accuracy through training to increase the birdies chance at each hole, which can lead to lower average strokes and increased possibility of being within T10.

Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies

BACKGROUND

In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy.

OBJECTIVE

This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD.

METHODS

The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC).

RESULTS

A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67.

CONCLUSIONS

The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings.

TRIAL REGISTRATION

PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779.

A network of networks approach for modeling interconnected brain tissue-specific networks

Motivation

Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes.

Results

We introduce a novel and efficient framework, called a ‘Network of Networks’ approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the ‘Network of Networks’ model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain.

Availability and implementation

Our software is available from https://github.com/infinite-point/GOSPEL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Power spectrum of spontaneous cerebral homodynamic oscillation shows a distinct pattern in autism spectrum disorder

Spontaneous hemodynamic fluctuations recorded by functional near-infrared spectroscopy (fNIRS) from bilateral temporal lobes were analyzed on 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. By frequency domain analysis, a new characteristic was uncovered that the power spectrum of low frequency cerebral hemodynamic oscillation showed a distinct pattern in ASD. More specifically, at the frequency of 0.0200 Hz, the power of oxygenated hemoglobin was larger for TD than ASD, whereas in the band of 0.0267-0.0333 Hz, the power of deoxygenated hemoglobin was larger for ASD than TD. Using these new features and those identified previously together as feature variables for the support vector machine (SVM) classifier, accurate classification between ASD and TD was achieved with a sensitivity of 90.2%, specificity of 95.1% and accuracy of 92.7%.

Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder

Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.

Superior Intraparietal Sulcus Controls the Variability of Visual Working Memory Precision

Limitations of working memory (WM) capacity depend strongly on the cognitive resources that are available for maintaining WM contents in an activated state. Increasing the number of items to be maintained in WM was shown to reduce the precision of WM and to increase the variability of WM precision over time. Although WM precision was recently associated with neural codes particularly in early sensory cortex, we have so far no understanding of the neural bases underlying the variability of WM precision, and how WM precision is preserved under high load. To fill this gap, we combined human fMRI with computational modeling of behavioral performance in a delayed color-estimation WM task. Behavioral results replicate a reduction of WM precision and an increase of precision variability under high loads (5 > 3 > 1 colors). Load-dependent BOLD signals in primary visual cortex (V1) and superior intraparietal sulcus (IPS), measured during the WM task at 2-4 s after sample onset, were modulated by individual differences in load-related changes in the variability of WM precision. Although stronger load-related BOLD increase in superior IPS was related to lower increases in precision variability, thus stabilizing WM performance, the reverse was observed for V1. Finally, the detrimental effect of load on behavioral precision and precision variability was accompanied by a load-related decline in the accuracy of decoding the memory stimuli (colors) from left superior IPS. We suggest that the superior IPS may contribute to stabilizing visual WM performance by reducing the variability of memory precision in the face of higher load.

SIGNIFICANCE STATEMENT

This study investigates the neural bases of capacity limitations in visual working memory by combining fMRI with cognitive modeling of behavioral performance, in human participants. It provides evidence that the superior intraparietal sulcus (IPS) is a critical brain region that influences the variability of visual working memory precision between and within individuals (Fougnie et al., 2012; van den Berg et al., 2012) under increased memory load, possibly in cooperation with perceptual systems of the occipital cortex. These findings substantially extend our understanding of the nature of capacity limitations in visual working memory and their neural bases. Our work underlines the importance of integrating cognitive modeling with univariate and multivariate methods in fMRI research, thus improving our knowledge of brain-behavior relationships.

Development of the uncinate fasciculus: Implications for theory and developmental disorders

The uncinate fasciculus (UF) is a long-range white matter tract that connects limbic regions in the temporal lobe to the frontal lobe. The UF is one of the latest developing tracts, and continues maturing into the third decade of life. As such, individual differences in the maturational profile of the UF may serve to explain differences in behavior. Indeed, atypical macrostructure and microstructure of the UF have been reported in numerous studies of individuals with developmental and psychiatric disorders such as social deprivation and maltreatment, autism spectrum disorders, conduct disorder, risk taking, and substance abuse. The present review evaluates what we currently know about the UF's developmental trajectory and reviews the literature relating UF abnormalities to specific disorders. Additionally, we take a dimensional approach and critically examine symptoms and behavioral impairments that have been demonstrated to cluster with UF aberrations, in an effort to relate these impairments to our speculations regarding the functionality of the UF. We suggest that developmental disorders with core problems relating to memory retrieval, reward and valuation computation, and impulsive decision making may be linked to aberrations in uncinate microstructure.

Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level

BACKGROUND AND PURPOSE

Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI).

METHODS

The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation.

RESULTS

The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70.

CONCLUSIONS

Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.

Single stimulus fMRI produces a neural individual difference measure for Autism Spectrum Disorder

Functional magnetic resonance imaging typically makes inferences about neural substrates of cognitive phenomena at the group level. We report the use of a single-stimulus BOLD response in the cingulate cortex that differentiates individual children with autism spectrum disorder from matched typically developing control children with sensitivity and specificity of 63.6% and 73.7% respectively. The approach consists of passive viewing of 'self' and 'other' faces from which an individual difference measure is derived from the BOLD response to the first 'self' image only; the method, penalized logistic regression, requires no averaging over stimulus presentations or individuals. These findings show that single-stimulus fMRI responses can be extracted from individual subjects and used profitably as a neural individual difference measure. The result suggests that single-stimulus fMRI can be developed to produce quantitative neural biomarkers for other developmental disorders and may even be useful in the rapid typing of cognition in healthy individuals.

Fusiform correlates of facial memory in autism

Prior studies have shown that performance on standardized measures of memory in children with autism spectrum disorder (ASD) is substantially reduced in comparison to matched typically developing controls (TDC). Given reported deficits in face processing in autism, the current study compared performance on an immediate and delayed facial memory task for individuals with ASD and TDC. In addition, we examined volumetric differences in classic facial memory regions of interest (ROI) between the two groups, including the fusiform, amygdala, and hippocampus. We then explored the relationship between ROI volume and facial memory performance. We found larger volumes in the autism group in the left amygdala and left hippocampus compared to TDC. In contrast, TDC had larger left fusiform gyrus volumes when compared with ASD. Interestingly, we also found significant negative correlations between delayed facial memory performance and volume of the left and right fusiform and the left hippocampus for the ASD group but not for TDC. The possibility of larger fusiform volume as a marker of abnormal connectivity and decreased facial memory is discussed.

Neuroimaging-based methods for autism identification: a possible translational application?

Classification methods based on machine learning (ML) techniques are becoming widespread analysis tools in neuroimaging studies. They have the potential to enhance the diagnostic power of brain data, by assigning a predictive index, either of pathology or of treatment response, to the single subject's acquisition. ML techniques are currently finding numerous applications in psychiatric illness, in addition to the widely studied neurodegenerative diseases. In this review we give a comprehensive account of the use of classification techniques applied to structural magnetic resonance images in autism spectrum disorders (ASDs). Understanding of these highly heterogeneous neurodevelopmental diseases could greatly benefit from additional descriptors of pathology and predictive indices extracted directly from brain data. A perspective is also provided on the future developments necessary to translate ML methods from the field of ASD research into the clinic.

Feature selection and classification of imbalanced datasets: application to PET images of children with autistic spectrum disorders

Learning with discriminative methods is generally based on minimizing the misclassification of training samples, which may be unsuitable for imbalanced datasets where the recognition might be biased in favor of the most numerous class. This problem can be addressed with a generative approach, which typically requires more parameters to be determined leading to reduced performances in high dimension. In such situations, dimension reduction becomes a crucial issue. We propose a feature selection/classification algorithm based on generative methods in order to predict the clinical status of a highly imbalanced dataset made of PET scans of forty-five low-functioning children with autism spectrum disorders (ASD) and thirteen non-ASD low functioning children. ASDs are typically characterized by impaired social interaction, narrow interests, and repetitive behaviors, with a high variability in expression and severity. The numerous findings revealed by brain imaging studies suggest that ASD is associated with a complex and distributed pattern of abnormalities that makes the identification of a shared and common neuroimaging profile a difficult task. In this context, our goal is to identify the rest functional brain imaging abnormalities pattern associated with ASD and to validate its efficiency in individual classification. The proposed feature selection algorithm detected a characteristic pattern in the ASD group that included a hypoperfusion in the right Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateral postcentral area. Our algorithm allowed for a significantly accurate (88%), sensitive (91%) and specific (77%) prediction of clinical category. For this imbalanced dataset, with only 13 control scans, the proposed generative algorithm outperformed other state-of-the-art discriminant methods. The high predictive power of the characteristic pattern, which has been automatically identified on whole brains without any priors, confirms previous findings concerning the role of STS in ASD. This work offers exciting possibilities for early autism detection and/or the evaluation of treatment response in individual patients.

A shift to randomness of brain oscillations in people with autism

BACKGROUND

Resting-state functional magnetic resonance imaging (fMRI) enables investigation of the intrinsic functional organization of the brain. Fractal parameters such as the Hurst exponent, H, describe the complexity of endogenous low-frequency fMRI time series on a continuum from random (H = .5) to ordered (H = 1). Shifts in fractal scaling of physiological time series have been associated with neurological and cardiac conditions.

METHODS

Resting-state fMRI time series were recorded in 30 male adults with an autism spectrum condition (ASC) and 33 age- and IQ-matched male volunteers. The Hurst exponent was estimated in the wavelet domain and between-group differences were investigated at global and voxel level and in regions known to be involved in autism.

RESULTS

Complex fractal scaling of fMRI time series was found in both groups but globally there was a significant shift to randomness in the ASC (mean H = .758, SD = .045) compared with neurotypical volunteers (mean H = .788, SD = .047). Between-group differences in H, which was always reduced in the ASC group, were seen in most regions previously reported to be involved in autism, including cortical midline structures, medial temporal structures, lateral temporal and parietal structures, insula, amygdala, basal ganglia, thalamus, and inferior frontal gyrus. Severity of autistic symptoms was negatively correlated with H in retrosplenial and right anterior insular cortex.

CONCLUSIONS

Autism is associated with a small but significant shift to randomness of endogenous brain oscillations. Complexity measures may provide physiological indicators for autism as they have done for other medical conditions.

Atypical diffusion tensor hemispheric asymmetry in autism

BACKGROUND

Biological measurements that distinguish individuals with autism from typically developing individuals and those with other developmental and neuropsychiatric disorders must demonstrate very high performance to have clinical value as potential imaging biomarkers. We hypothesized that further study of white matter microstructure (WMM) in the superior temporal gyrus (STG) and temporal stem (TS), two brain regions in the temporal lobe containing circuitry central to language, emotion, and social cognition, would identify a useful combination of classification features and further understand autism neuropathology.

METHODS

WMM measurements from the STG and TS were examined from 30 high-functioning males satisfying full criteria for idiopathic autism aged 7-28 years and 30 matched controls and a replication sample of 12 males with idiopathic autism and 7 matched controls who participated in a previous case-control diffusion tensor imaging (DTI) study. Language functioning, adaptive functioning, and psychotropic medication usage were also examined.

RESULTS

In the STG, we find reversed hemispheric asymmetry of two separable measures of directional diffusion coherence, tensor skewness, and fractional anisotropy. In autism, tensor skewness is greater on the right and fractional anisotropy is decreased on the left. We also find increased diffusion parallel to white matter fibers bilaterally. In the right not left TS, we find increased omnidirectional, parallel, and perpendicular diffusion. These six multivariate measurements possess very high ability to discriminate individuals with autism from individuals without autism with 94% sensitivity, 90% specificity, and 92% accuracy in our original and replication samples. We also report a near-significant association between the classifier and a quantitative trait index of autism and significant correlations between two classifier components and measures of language, IQ, and adaptive functioning in autism.

Microsurgical anatomy of the temporal stem: clinical relevance and correlations with diffusion tensor imaging fiber tracking

Object

The authors used a fiber dissection technique to describe the temporal stem and explain the tendency of malignant tumors to spread within both the frontal and temporal lobes. The authors focused on the morphological characteristics and course of various fasciculi of the temporal stem, including the uncinate fascicle, occipitofrontal fascicle, anterior commissure, loop of the optic radiations (Meyer loop), and the ansa peduncularis.

Methods

Eight previously frozen, formalin-fixed human brains were dissected under an operating microscope using the fiber dissection technique described by Klingler. Lateral, inferior, and medial approaches were made. Cross-sectional 3D MR images obtained in 10 patients without brain lesions demonstrated that fibers of the temporal stem, which were intermingled together in various ways, curved laterally within the basal forebrain. Various pathological entities affecting the temporal stem are described and discussed.

Results

The uncinate fascicle has 3 portions: a ventral extension, an intermediary segment called the isthmus, and a dorsal segment. The inferior occipitofrontal fasciculus is a layer of more superficial white matter that appeared to be superior to the uncinate fasciculus. A short ventral portion of the radiations of the corpus callosum was sometimes noted to run ventrally to enter the temporal stem and to reach both temporal lobes.

Conclusions

To the authors' knowledge, a detailed anatomy of the temporal stem has not been previously described in the literature. The unique anatomy of the temporal stem provides a route for tumor spread between the frontal and temporal lobes.

3D cerebral cortical morphometry in autism: increased folding in children and adolescents in frontal, parietal, and temporal lobes

This paper presents a systematic evaluation of cortical folding, or complexity, in autism. It introduces two novel measures to analyze folding in a specific region of interest, which, unlike traditional measures, produce an intuitive easily-interpretable description of folding and inform the nature of folding change by incorporating local surface-patch orientation. This study reports new findings of increased cortical folding in autistics in the frontal, parietal, and temporal lobes, as compared to controls. These differences are stronger in children than adolescents. The paper validates part of the findings using the new measures based on comparisons with traditional measures. Unlike studies in the literature, this paper reports new findings, via a fully 3D folding analysis on all brain lobes, based on the consensus of virtually all 6 folding measures used (2 new, 4 traditional) via rigorous statistical permutation testing. In these ways, this paper not only strengthens some previous clinical findings, but also extends the state of the art in autism research.

Diffusion tensor imaging of white matter in the superior temporal gyrus and temporal stem in autism

Recent MRI studies have indicated that regions of the temporal lobe including the superior temporal gyrus (STG) and the temporal stem (TS) appear to be abnormal in autism. In this study, diffusion tensor imaging (DTI) measurements of white matter in the STG and the TS were compared in 43 autism and 34 control subjects. DTI measures of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were compared between groups. In all regions, fractional anisotropy was significantly decreased and both mean diffusivity and radial diffusivity were significantly increased in the autism group. These results suggest that white matter microstructure in autism is abnormal in these temporal lobe regions, which is consistent with theories of aberrant brain connectivity in autism.