Persistent craniopharyngeal canal with an associated sphenoid sinus fistula.

Persistent craniopharyngeal canal (PCC) is a rare congenital anomaly that appears as a linear well-corticated canal running from the sella through the clivus and into the nasopharynx. Case reports of this anomaly have shown it is associated with a range of craniofacial defects, pituitary abnormalities, and meningoencephaloceles. It predisposes patients to bacterial meningitis. In this case a 46-year-old gentleman presenting for preoperative planning for surgical drainage of Potts Puffy tumor was found to have a PCC on CT and MRI. Imaging also demonstrated the presence of chronic inflammation and a fistula extending from the tract into the sphenoid sinus. This unusual presentation of a PCC with a sphenoid sinus fistula broadens the potential clinical presentations of PCC and further emphasizes the ability of this anomaly to serve as a conduit for CNS infection.

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.

Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi-run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p<.001). Effects of motion, age, handedness, blood pressure, weight, and brain volume on gender prediction are discussed. Further, we found that FC features within the default mode (DMN), fronto-parietal and sensorimotor networks contributed most to gender prediction. In the DMN, right fusiform gyrus and right ventromedial prefrontal cortex were important contributors. The above regions have been previously implicated in aspects of social functioning and this suggests potential gender differences in social cognition mediated by the DMN. Our findings demonstrate that gender can be reliably predicted using rfMRI data and highlight the importance of controlling for gender in brain imaging studies.

Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism.

Previous studies applying automatic preprocessing methods on Structural Magnetic Resonance Imaging (sMRI) report inconsistent neuroanatomical abnormalities in Autism Spectrum Disorder (ASD). In this study we investigate inter-method differences as a possible cause behind these inconsistent findings. In particular, we focus on the estimation of the following brain volumes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intra cranial volume (TIV). T1-weighted sMRIs of 417 ASD subjects and 459 typically developing controls (TDC) from the ABIDE dataset were estimated using three popular preprocessing methods: SPM, FSL, and FreeSurfer (FS). Brain volumes estimated by the three methods were correlated but had significant inter-method differences; except TIVSPM vs. TIVFS, all inter-method differences were significant. ASD vs. TDC group differences in all brain volume estimates were dependent on the method used. SPM showed that TIV, GM, and CSF volumes of ASD were larger than TDC with statistical significance, whereas FS and FSL did not show significant differences in any of the volumes; in some cases, the direction of the differences were opposite to SPM. When methods were compared with each other, they showed differential biases for autism, and several biases were larger than ASD vs. TDC differences of the respective methods. After manual inspection, we found inter-method segmentation mismatches in the cerebellum, sub-cortical structures, and inter-sulcal CSF. In addition, to validate automated TIV estimates we performed manual segmentation on a subset of subjects. Results indicate that SPM estimates are closest to manual segmentation, followed by FS while FSL estimates were significantly lower. In summary, we show that ASD vs. TDC brain volume differences are method dependent and that these inter-method discrepancies can contribute to inconsistent neuroimaging findings in general. We suggest cross-validation across methods and emphasize the need to develop better methods to increase the robustness of neuroimaging findings.

Characterization of Face-Selective Patches in Orbitofrontal Cortex.

Face processing involves a complex, multimodal brain network. While visual-perceptual face patches in posterior parts of the brain have been studied for over a decade, the existence and properties of face-selective regions in orbitofrontal cortex (OFC) is a relatively new area of research. While regions of OFC are implicated in the emotional processing of faces, this is typically interpreted as a domain-general response to affective value rather than a face- or socially-specific response. However, electrophysiology studies in monkeys have identified neurons in OFC that respond more to faces than any other stimuli. Here, we characterize the prevalence and location of OFC face-selective regions in 20 healthy college students. We did this by including another biologically motivating category (appetizing foods) in a variant of the standard face localizer. Results show that face-selective patches can be identified at the individual level. Furthermore, in both a region of interest (ROI) and a whole brain analysis, medial regions of the OFC were face-selective, while lateral regions were responsive to faces and foods, indicating a domain-general response in lateral OFC. Medial OFC (mOFC) response to faces scales in relationship to a measure of social motivation that is distinct from face processing abilities associated with fusiform cortex.

Asymmetry of fusiform structure in autism spectrum disorder: trajectory and association with symptom severity.

BACKGROUND: While asymmetry in the fusiform gyrus (FFG) has been reported in functional and structural studies in typically developing controls (TDC), few studies have examined FFG asymmetry in autism spectrum disorder (ASD) subjects and those studies are limited by small sample sizes, and confounded by cognitive ability or handedness. No previous work has examined FFG surface area or cortical thickness asymmetry in ASD; nor do we understand the trajectory of FFG asymmetry over time. Finally, it is not known how FFG structural asymmetry relates to ASD symptom severity. METHODS: In this study, we examined FFG volume, surface area, and cortical thickness asymmetry, as well as their cross-sectional trajectories in a large sample of right-handed males aged 7 to 25 years with 128 ASD and 127 TDC subjects using general linear models. In addition, we examined the relationship between FFG asymmetry and ASD severity using the Autism Diagnostic Observation Schedule (ADOS) and Gotham autism severity scores. RESULTS: Findings revealed that while group differences were evident with mean leftward asymmetry in ASD and mean near symmetry in TDC volume and surface area, asymmetry for both groups existed on a spectrum encompassing leftward and rightward asymmetry. In ASD subjects, volume asymmetry was negatively associated with ADOS and autism severity score symptom measures, with a subset of rightward asymmetric patients being most severely affected. We also observed differential trajectory of surface area asymmetry: ASD subjects exhibited a change from leftward asymmetry toward symmetry from age 7 to 25, whereas TDCs exhibited the reverse trend with a change from near symmetry toward leftward symmetry over the observed age range. CONCLUSIONS: Abnormalities in FFG structural asymmetry are related to symptom severity in ASD and show differential developmental trajectory compared to TDC. This study is the first to note these findings. These results may have important implications for understanding the role of FFG asymmetry in ASD.

A Comparison of Structural Brain Imaging Findings in Autism Spectrum Disorder and Attention-Deficit Hyperactivity Disorder.

ASD and ADHD are regarded as distinct disorders in the current DSM-5. However, recent research and the RDoC initiative are recognizing considerable overlap in the clinical presentation of ASD, ADHD, and other neurodevelopmental disorders. In spite of numerous neuroimaging findings in ASD and ADHD, the extent to which either of the above views are supported remains equivocal. Here we compare structural MRI and DTI literature in ASD and ADHD. Our main findings reveal both distinct and shared neural features. Distinct expressions were in total brain volume (ASD: increased volume, ADHD: decreased volume), amygdala (ASD: overgrowth, ADHD: normal), and internal capsule (ASD: unclear, ADHD: reduced FA in DTI). Considerable overlap was noted in the corpus callosum and cerebellum (lower volume in structural MRI and decreased FA in DTI), and superior longitudinal fasciculus (reduced FA in DTI). In addition, we identify brain regions which have not been studied in depth and require more research. We discuss relationships between brain features and symptomatology. We conclude by addressing limitations of current neuroimaging research and offer approaches that account for clinical heterogeneity to better distinguish brain-behavior relationships.