Altered resting-state functional connectivity of default mode network in brachioradial pruritus.

BACKGROUND: Patients with chronic pruritus (CP) have a low quality of life, thus it is important to gain a better understanding of the underlying processes. Previous functional magnetic resonance imaging studies at rest (rsfMRI) have shown that mainly areas associated with the default mode network (DMN), sensorimotor (SMN), frontoparietal (FPN) and salience networks (SN) are involved in the processing of itch in patients with chronic pruritus (CP), as well as the cortico-striatal circuit, which is involved in the motoric preparation of scratching. rsfMRI studies on functional connectivity (FC) patterns of resting-state networks (RSNs) in patients with inflammatory atopic dermatitis (AD) or with neuropathic brachioradial pruritus (BRP) compared with healthy controls (HC) are lacking. OBJECTIVES: The main goals of this study were to investigate whether functional connectivity within networks and areas associated with itch detection and processing are altered in patients with AD and BRP compared with matched healthy controls by rsfMRI, respectively. METHODS: Patients with AD (n = 28) and with BRP (n = 28) were compared with corresponding matched healthy controls by rsfMRI. Group-specific RSNs were identified by independent component analysis (ICA) and between-group differences in the RSNs were analysed by dual regression technique. Seed-based functional connectivity was analysed in several itch-related brain regions belonging to the DMN, SN and FPN, respectively. RESULTS: ICA and seed-based analyses revealed decreased functional connectivity in BRP compared with HC specially within the DMN including the precuneus and cingulate cortex. For AD patients in comparison with HC, as well as when BRP and AD patients were compared directly, no significant FC differences at rest were seen. CONCLUSIONS: Our findings point towards decreased FC particularly in the DMN at rest in patients with BRP. These results seem to indicate that central connectivity patterns at rest differentially encode itch in BRP and AD.

Interaction of Developmental Venous Anomalies with Resting-State Functional MRI Measures.

BACKGROUND AND PURPOSE: Functional MR imaging of the brain, used for both clinical and neuroscientific applications, relies on measuring fluctuations in blood oxygenation. Such measurements are susceptible to noise of vascular origin. The purpose of this study was to assess whether developmental venous anomalies, which are frequently observed normal variants, can bias fMRI measures by appearing as true neural signal. MATERIALS AND METHODS: Large developmental venous anomalies (1 in each of 14 participants) were identified from a large neuroimaging cohort (n = 814). Resting-state fMRI data were decomposed using independent component analysis, a data-driven technique that creates distinct component maps representing aspects of either structured noise or true neural activity. We searched all independent components for maps that exhibited a spatial distribution of their signals following the topography of developmental venous anomalies. RESULTS: Of the 14 developmental venous anomalies identified, 10 were clearly present in 17 fMRI independent components in total. While 9 (52.9%) of these 17 independent components were dominated by venous contributions and 2 (11.8%) by motion artifacts, 2 independent components (11.8%) showed partial neural signal contributions and 5 independent components (29.4%) unambiguously exhibited typical neural signal patterns. CONCLUSIONS: Developmental venous anomalies can strongly resemble neural signal as measured by fMRI. They are thus a potential source of bias in fMRI analyses, especially when present in the cortex. This could impede interpretation of local activity in patients, such as in presurgical mapping. In scientific studies with large samples, developmental venous anomaly confounds could be mainly addressed using independent component analysis-based denoising.

Multivariate classification of blood oxygen level-dependent FMRI data with diagnostic intention: a clinical perspective.

SUMMARY: There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-of-concept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews.