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Aim: Impulse‐control disorder is a common neuropsychiatric complication in Parkinson's disease (PD) under dopamine replacement therapy. Prior studies tested the balance between enhanced desire towards reward and cognitive control deficits, hypothesized to be biased towards the former in impulse control disorders. We provide evidence for this hypothesis by measuring behavioral and neural patterns behind the influence of sexual desire over response inhibition and tools towards functional restoration using repetitive transcranial stimulation in patients with hypersexuality as predominant impulsive disorder. Methods: The effect of sexual cues on inhibition was measured with a novel erotic stop‐signal task under on and off dopaminergic medication. Task‐related functional and anatomical connectivity models were estimated in 16 hypersexual and 17 nonhypersexual patients with PD as well as in 17 healthy controls. Additionally, excitatory neuromodulation using intermittent theta‐burst stimulation (sham‐controlled) was applied over the pre‐supplementary motor area in 20 additional hypersexual patients with PD aiming to improve response inhibition. Results: Compared with their nonhypersexual peers, patients with hypersexuality recruited caudate, pre‐supplementary motor area, ventral tegmental area, and anterior cingulate cortex while on medication. Reduced connectivity was found between pre‐supplementary motor area and caudate nucleus in hypersexual compared with nonhypersexual patients (while medicated), a result paralleled by compensatory enhanced anatomical connectivity. Furthermore, stimulation over the pre‐supplementary motor area improved response inhibition in hypersexual patients with PD when exposed to sexual cues. Conclusion: This study, therefore, has identified a specific fronto‐striatal and mesolimbic circuitry underlying uncontrolled sexual responses in medicated patients with PD where cortical neuromodulation halts its expression. [ABSTRACT FROM AUTHOR]

Identifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution. We enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline. At baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients. Neuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ 42) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF A β 42 and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease. • A data-driven approach was proposed for prediction and timeline estimation. • A reduce vector of markers was selected for training the models. • High performance was achieved in detecting subjects of progressing to cognitive impaired. • The timeline towards dementia was checked by diagnosing patients and observing clinical events. • Amyloid pathology in PD subjects does not significantly accelerate cognitive decline. [ABSTRACT FROM AUTHOR]

Global structural brain connectivity has been reported to be sex-dependent with women having increased interhemispheric connectivity (InterHc) and men having greater intrahemispheric connectivity (IntraHc). However, (a) smaller brains show greater InterHc, (b) larger brains show greater IntraHc, and (c) women have, on average, smaller brains than men. Therefore, sex differences in brain size may modulate sex differences in global brain connectivity. At the behavioural level, sex-dependent differences in connectivity are thought to contribute to men-women differences in spatial and verbal abilities. But this has never been tested at the individual level. The current study assessed whether individual differences in global structural connectome measures (InterHc, IntraHc and the ratio of InterHc relative to IntraHc) predict spatial and verbal ability while accounting for the effect of sex and brain size. The sample included forty men and forty women, who did neither differ in age nor in verbal and spatial latent components defined by a broad battery of tests and tasks. High-resolution T 1 -weighted and diffusion-weighted images were obtained for computing brain size and reconstructing the structural connectome. Results showed that men had higher IntraHc than women, while women had an increased ratio InterHc/IntraHc. However, these sex differences were modulated by brain size. Increased InterHc relative to IntraHc predicted higher spatial and verbal ability irrespective of sex and brain size. The positive correlations between the ratio InterHc/IntraHc and the spatial and verbal abilities were confirmed in 1000 random samples generated by bootstrapping. Therefore, sex differences in global structural connectome connectivity were modulated by brain size and did not underlie sex differences in verbal and spatial abilities. Rather, the level of dominance of InterHc over IntraHc may be associated with individual differences in verbal and spatial abilities in both men and women. [ABSTRACT FROM AUTHOR]

Synaptic dysfunction is a core deficit in Alzheimer's disease, preceding hallmark pathological abnormalities. Resting-state magnetoencephalography (MEG) was used to assess whether functional connectivity patterns, as an index of synaptic dysfunction, are associated with CSF biomarkers [i.e., phospho-tau (p-tau) and amyloid beta (Aβ42) levels]. We studied 12 human subjects diagnosed with mild cognitive impairment due to Alzheimer's disease, comparing those with normal and abnormal CSF levels of the biomarkers. We also evaluated the association between aberrant functional connections and structural connectivity abnormalities, measured with diffusion tensor imaging, as well as the convergent impact of cognitive deficits and CSF variables on network disorganization. One-third of the patients converted to Alzheimer's disease during a follow-up period of 2.5 years. Patients with abnomal CSF p-tau and Aβ42 levels exhibited both reduced and increased functional connectivity affecting limbic structures such as the anterior/posterior cingulate cortex, orbitofrontal cortex, and medial temporal areas in different frequency bands. A reduction in posterior cingulate functional connectivity mediated by p-tau was associated with impaired axonal integrity of the hippocampal cingulum. We noted that several connectivity abnormalities were predicted by CSF biomarkers and cognitive scores. These preliminary results indicate that CSF markers of amyloid deposition and neuronal injury in early Alzheimer's disease associate with a dual pattern of cortical network disruption, affecting key regions of the default mode network and the temporal cortex. MEG is useful to detect early synaptic dysfunction associated with Alzheimer's disease brain pathology in terms of functional network organization. [ABSTRACT FROM AUTHOR]

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer's disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level.pMCIpatients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchronization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD. [ABSTRACT FROM AUTHOR]