Differential regulation of BK channels by fragile X mental retardation protein.

Fragile X mental retardation protein (FMRP) is an RNA-binding protein prominently expressed in neurons. Missense mutations or complete loss of FMRP can potentially lead to fragile X syndrome, a common form of inherited intellectual disability. In addition to RNA regulation, FMRP was also proposed to modulate neuronal function by direct interaction with the large conductance Ca2+- and voltage-activated potassium channel (BK) ß4 regulatory subunits (BKß4). However, the molecular mechanisms underlying FMRP regulation of BK channels were not studied in detail. We have used electrophysiology and super-resolution stochastic optical reconstruction microscopy (STORM) to characterize the effects of FMRP on pore-forming BKα subunits, as well as the association with regulatory subunits BKß4. Our data indicate that, in the absence of coexpressed ß4, FMRP alters the steady-state properties of BKα channels by decreasing channel activation and deactivation rates. Analysis using the Horrigan-Aldrich model revealed alterations in the parameters associated with channel opening (L0) and voltage sensor activation (J0). Interestingly, FMRP also altered the biophysical properties of BKαß4 channels favoring channel opening, although not as dramatically as BKα. STORM experiments revealed clustered multi-protein complexes, consistent with FMRP interacting not only to BKαß4 but also to BKα. Lastly, we found that a partial loss-of-function mutation in FMRP (R138Q) counteracts many of its functional effects on BKα and BKαß4 channels. In summary, our data show that FMRP modulates the function of both BKα and BKαß4 channels.

Automatic brain caudate nuclei segmentation and classification in diagnostic of Attention-Deficit/Hyperactivity Disorder.

We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.