Biology subjects

Queirazza, F.

Publications and source records attributed to Queirazza, F..

3 recordsLinked to original sources

bioRxiv2024

Delineating In-Vivo T1-Weighted Intensity Profiles Within the Human Insula Cortex Using 7-Tesla MRI

The integral role of the insula cortex in sensory and cognitive function has been well documented in humans, and fine anatomical details characterising the insula have been extensively investigated ex-vivo in both human and non-human primates. However, in-vivo studies of insula anatomy in humans (in general), and within-insula parcellation (in particular) have been limited. The current study leverages 7 Tesla magnetic resonance imaging to delineate cortical depth intensity profiles within the human cortex. Our analysis revealed two separate clusters of relatively high and low signal intensity across the insula cortex located in three distinct compartments within the posterior, anterior-inferior, and middle insula. The posterior and anterior-inferior compartments are characterised by elevated T1-weighted signal intensities, contrasting with lower intensity observed in the middle insular compartment, compatible with ex-vivo studies. Importantly, the detection of the high T1-weighted anterior cluster is determined by the choice of brain atlas employed to define the insular ROI. We obtain reliable in-vivo within-insula parcellation at the individual and group levels, across two separate cohorts acquired in two separate sites (n1 = 21, Glasgow, UK; n2 = 101, Amsterdam, NL). Results are further confirmed by deriving cortical depth dependent profiles from T1Map and R1Map images. These results reflect new insights into the insula anatomical structure, in-vivo, while highlighting the use of 7 Tesla in neuroimaging with potential implications for individualised medicine approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/605123v3_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1c27244org.highwire.dtl.DTLVardef@dbd256org.highwire.dtl.DTLVardef@1ce44f6org.highwire.dtl.DTLVardef@11183d0_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2017

Dorsomedial prefrontal cortex activity during reinforcement learning discriminates response to Cognitive Behavioural Therapy in depression.

BACKGROUNDCognitive behavioural therapy (CBT) is an effective evidence based treatment for depression. At present there is no reliable predictor of CBT in depression. Although the key to successful CBT in depression lies in altering maladaptive information processing, no previous imaging study has probed predictors of CBT response using pre-treatment neural encoding of information processing. METHODS: Using functional magnetic resonance imaging we scanned 37 unmedicated depressed subjects before and after completing computerised CBT (cCBT). We model the trial-by-trial appraisal of feedback information during a probabilistic learning task by means of a dynamic learning rate. To discriminate response to cCBT we capitalise on the pre-treatment blood oxygen level dependent (BOLD) activity encoding the dynamic learning rate as a function of feedback congruence and valence. Additionally, we probe between-group differences in the learning style encoded in the models parameters.\n\nRESULTSWe show BOLD activity in the dorsomedial prefrontal cortex (dmPFC) to be encoding the dynamic learning rate. Crucially, responders exhibit greater BOLD activity in the dmPFC during incongruent negative trials but lower BOLD activity during congruent negative trials than non-responders. Additionally, on between-group comparisons of models parameter estimates we show responders take relatively greater account of previous feedback history and make comparatively smaller adjustments to the learning rate as a result of outcome surprisingness.\n\nCONCLUSIONSOur findings provide novel and important insights into the cognitive mechanisms underpinning response to cCBT and lend support to the feasibility and validity of neurocomputational approaches to treatment prediction research in psychiatry.

neuroscience↗

bioRxiv2016

Spatiotemporal characterization of the neural correlates of outcome valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. Two separate outcome dimensions, namely the valence (positive or negative) and surprise (the absolute degree of deviation from expectations) of an outcome are thought to subserve adaptive decision-making and learning, however their neural correlates and relative contribution to learning remain debated. Here, we coupled single-trial analyses of electroencephalography with simultaneously acquired fMRI, while participants performed a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of outcome valence and surprise in the human brain. Electrophysiological variability in outcome valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Variability in outcome surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations we also found a linear superposition of the two outcome dimensions in a smaller network encompassing visuo-mnemonic and reward areas. This spatiotemporal overlap was uniquely exposed by our EEG-informed fMRI approach. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning.

neuroscience↗