Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Computational neuroimaging strategies for single patient predictions

AbstractNeuroimaging increasingly exploits machine learning techniques in an attempt to achieve clinically relevant single-subject predictions. An alternative to machine learning, which tries to establish predictive links between features of the observed data and clinical variables, is the deployment of computational models for inferring on the (patho)physiological and cognitive mechanisms that generate behavioural and neuroimaging responses. This paper discusses the rationale behind a computational approach to neuroimaging-based single-subject inference, focusing on its potential for characterising disease mechanisms in individual subjects and mapping these characterisations to clinical predictions. Following an overview of two main approaches – Bayesian model selection and generative embedding – which can link computational models to individual predictions, we review how these methods accommodate heterogeneity in psychiatric and neurological spectrum disorders, help avoid erroneous interpretations of neuroimaging data, and establish a link between a mechanistic, model-based approach and the statistical perspectives afforded by machine learning

Assessing motor and cognitive function to detect shifts in brain function in two models of Parkinson\u27s disease

Cognitive changes accompany and often precede the onset of classic motor deficits typical of Parkinson&;#8217;s disease. A current focus of Parkinson&;#8217;s research has become understanding the development and progression of pre-motor cognitive changes. Based on previous research showing that hippocampus-sensitive spatial learning can be enhanced at the cost of impaired striatum-sensitive response learning, we hypothesized that changes in the balance between these two cognitive systems could be used as a proxy for the relative strength or health of their associated brain regions. Because non-motor symptoms of Parkinson&;#8217;s disease can precede the onset of the diagnostic motor dysfunction, changes in the balance between distinct learning strategies may represent an early marker of Parkinson’s-related neurodegeneration. Two rat models were used to assess the relationship between Parkinson’s disease-related motor dysfunction and changes in cognition. In the first study, a 6-OHDA rat model of Parkinson’s disease was used to generate a partial lesion of dopaminergic neurons in the nigrostriatal pathway. Despite the probable depletion of dopamine in the nigrostriatal pathway of lesioned rats that presented as impairment in two motor tasks, rats showed enhanced performance on the cognitive spontaneous alternation task, a test of spatial working memory. However, recent data suggest that multiple brain regions, including both the hippocampus and striatum, are activated during performance of the spontaneous alternation task; Parkinson’s-induced enhancements on this task may not be due solely to a shift in cognitive balance. Previous data show that inactivation of the hippocampus can enhance striatum-sensitive learning; however, it is unclear if inactivation of the striatum enhances hippocampus-sensitive functions. Prior to determining the effect of a 6-OHDA-induced lesion on hippocampus-sensitive learning, we wanted first to assess how impairing striatum function modulated place learning to determine cognitive shifts in rats with an intact brain. The second study uses two single-solution cognitive tasks that may link more closely to activation of separate neural systems. Temporary inhibition of the dorsal striatum by the GABAA receptor agonist, muscimol, produced deficits in motor function similar to those seen in the 6-OHDA model of Parkinson’s. Intrastriatal muscimol also impaired learning on a striatum-sensitive response learning task, suggesting that striatum-sensitive motor processes may overlap with striatum-sensitive cognitive processes. However, muscimol-induced striatum dysregulation did not produce enhancements on a hippocampus-sensitive spatial learning task. It is possible that the cognitive enhancements in hippocampus-sensitive processes are maximized when only specific neurotransmitter systems are dampened, such as the loss of dopaminergic signaling seen in Parkinson’s disease. Unlike 6-OHDA, which targets dopaminergic neurons, muscimol activates GABAA receptors, leading to the opening of Cl- channels, altering membrane potentials, and changing the likelihood of neurotransmitter release. Thus, activation of GABAA receptors by muscimol will alter neuron activity regardless of neurotransmitter system while 6-OHDA must initially affect dopaminergic neurons. Consequently, it is possible that muscimol decreases activity in neurotransmitter systems that play a compensatory role following 6-OHDA-induced dopaminergic degeneration. As such, a generalized inhibitor of neural activity like muscimol, may disrupt neural processes that are integral for seeing the Parkinson’s disease-related cognitive enhancements

Does practice of multi-directional stepping with auditory stimulation improve movement performance in patients with Parkinson\u27s disease

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder causing many physical limitations. Rhythmic auditory stimulation (RAS) influences motor complications not alleviated by medicine and has been used to modify straight line walking in this population. However, motor complications are exacerbated during more complex movements including those involving direction changes. Thus immediate RAS effects on direction switch duration (DSD) and other kinematic measures during a multi-directional step task were investigated in PD patients. Long term RAS application was also explored by evaluating functional gait and balance and kinematic step measures before and after 6 weeks of multi-directional stepping either with (Cue, C group) or without (No cue, NC group) RAS use. Evaluations were also administered 1, 4 and 8 weeks after training termination. Kinematic measures were collected during stepping without, then with RAS for the C group and without RAS for the NC group. Step testing/training was performed at slow, normal and fast speeds in forward, back and side directions. Participants with PD switched step direction during the stepping task faster with RAS use before training. Like straight line walking RAS application influenced the more complex task of direction switching and counteracted the well-known bradykinesia in PD. After training both groups improved their functional gait and balance measures and maintained balance improvements for at least 8 weeks. Only the C group retained gait improvements for at least 8 weeks after training termination. Adding RAS resulted in functional benefits not observed in training without it. Kinematic measures compared before and after step training clarified the underlying contributors to functional performances. Both groups reduced the variability of DSD. The C group participants maintained this alteration longer. DSD reduction also occurred after training and was retained for at least 8 weeks for this group. These outcomes further support the advantages of adding RAS to training regiments for those with PD. The current results indicate that RAS effects are not limited to simple activities like straight line walking. Moreover, RAS can be used for improving and maintaining improvements longer in activities involving various forms of transition which present most difficulties for those with PD

Anxiety in Parkinson's disease: relation to cognition and potential of non-pharmacological interventions

In addition to the classic motor symptoms, Parkinson’s disease (PD) causes a variety of non-motor symptoms that compromise quality of life and daily functioning. Anxiety, in particular, is prevalent and debilitating, but under-studied and under-treated. There is urgent need to understand the relation of anxiety to other non-motor symptoms, and to develop evidence-based treatments. Cognitive-behavioral therapy (CBT) and aerobic and resistance exercise are promising non-pharmacological treatment strategies for anxiety in PD, with potential to simultaneously reduce additional PD symptoms. Study 1 assessed a large sample of non-demented individuals with mild to moderate PD (N=77) and examined the relation between self-reported anxiety (Beck Anxiety Inventory [BAI]) and cognition with a focus on executive function and attention (Trail Making, Verbal Fluency, Digit Span). The majority of participants reported subclinical symptoms of anxiety (BAI ≤18). Higher anxiety correlated with poorer set-shifting, as well as with more advanced disease stage and severity. Study 2 implemented a single-case experimental design to evaluate the utility and feasibility of a 12-week cognitive-behavioral intervention for individuals with PD who also met criteria for a DSM-5 anxiety disorder (N=9). Weekly therapy sessions were conducted in-person (N=5) or via secure videoconferencing (N=4). At post-treatment, five participants reported significant reductions in anxiety and two additional participants reported significant reductions in comorbid depression. Most improvements were maintained at 6-week follow-up. Effects of CBT on secondary outcome measures (e.g., cognition, motor symptoms, sleep) varied widely across participants. Adherence and retention were high, as was satisfaction with treatment. Study 3 reviewed the effects of aerobic and resistance exercise on disturbances of mood, cognition, and sleep in PD and healthy adults. The literature supports aerobic and resistance exercise as feasible and promising adjunct treatments for mood, cognition, and sleep in PD, contingent upon additional exercise research that systematically targets non-motor symptom outcomes. Together these studies show that even subclinical anxiety is associated with cognitive disturbance in mild-moderate PD, and provide preliminary evidence for the effectiveness of CBT (in-person and internet-delivered), as well as aerobic and resistance exercise, as encouraging and viable treatments for anxiety in this disorder.2019-09-29T00:00:00

Neurobiological mechanisms of hallucinations in schizophrenia

All perception is a construct of the brain. Yet occasionally, sensory constructions emerge without origin in the physical world and are experienced as hallucinations. Hallucinations occur transdiagnostically, cross-culturally, and in all sensory modalities. They are common in people with schizophrenia, presenting in 60-80% of patients. Despite over 20 years of active neuroimaging research on hallucinations, the neural systems supporting these anomalous perceptual experiences remain disputed. This dissertation investigates the neurobiology of hallucinations, integrating research across structural and functional magnetic resonance imaging (MRI) to elucidate how hallucinations, chiefly in the context of schizophrenia, are supported by the brain, drawing on MRI indices of neurodevelopment. I introduce the phenomenon of hallucinations and motivate the utility of MRI for studying hallucinations. Considering their prevalence in other medical conditions, I conduct a meta-analysis and systematic review of the structural brain basis of hallucinations across diagnoses, primarily schizophrenia spectrum disorders and Parkinson’s disease. This illustrated distinct neuroanatomical organizations of grey matter associated with hallucinations that occur in neurodevelopmental compared to neurodegenerative disorders, which I hypothesise constitute at least two distinct mechanisms. Focussing on the neurodevelopmental mechanism characterized by fronto-temporal and insular grey matter reductions, I turn to the contribution of cortical sulcation, a product of second and third trimester neurodevelopmental processes, which has been robustly implicated in schizophrenia pathology, and, more recently, in hallucinations. Sulcal patterns derived from structural MRI provide a proxy in adulthood for early brain development. I studied two independent datasets of patients with schizophrenia who underwent clinical assessment and 3T MRI from the United Kingdom and Shanghai, China, stratified into those with and without hallucinations, and healthy controls from Shanghai. I first replicate the finding that left hemisphere paracingulate sulcus (PCS) length is reduced in patients who experience hallucinations, then demonstrate similar associations for superior temporal sulcus depth. Length and depth alterations occurred with focal deviations in sulcal geometry. The interindividual and interhemispheric variability of the PCS necessitated the development of semi-automated methods to characterize its morphology and validation to a manual protocol. I used structural covariance networks of the local gyrification index to investigate how specific sulcal deviations relate to global neurodevelopmental coordination, demonstrating that hallucinations correspond to increased covariance within and between salience and auditory networks. Hypothesizing structure-function relationships, I analyse resting-state functional MRI data from the same datasets described, finding significant interactions between PCS length and hallucinations status, but no main effects. There were no effects of hallucination status on salience and auditory network connectivity or in graph theoretical measures of connectivity, suggesting that resting-state connectivity is not a trait marker for hallucinations. Together, the discovery of neurodevelopmental alterations contributing to hallucinations provides mechanistic insight into the pathological consequences of prenatal origins. The interaction of sulcal alterations and hallucination status are associated with connectivity, which may have a role in the pathophysiology of hallucinations. I provide clear predictions and recommendations for future research.Gates Cambridge Scholarshi