Interpreting weight maps in terms of cognitive or clinical neuroscience: nonsense?

Since machine learning models have been applied to neuroimaging data, researchers have drawn conclusions from the derived weight maps. In particular, weight maps of classifiers between two conditions are often described as a proxy for the underlying signal differences between the conditions. Recent studies have however suggested that such weight maps could not reliably recover the source of the neural signals and even led to false positives (FP). In this work, we used semi-simulated data from ElectroCorticoGraphy (ECoG) to investigate how the signal-to-noise ratio and sparsity of the neural signal affect the similarity between signal and weights. We show that not all cases produce FP and that it is unlikely for FP features to have a high weight in most cases.Comment: conference articl

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

What does brain response to neutral faces tell us about major depression? Evidence from machine learning and fMRI

Introduction A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. Methods Two independent depressed samples: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier’s predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Results Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Conclusions Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients

What Does Brain Response to Neutral Faces Tell Us about Major Depression? Evidence from Machine Learning and fMRI

Introduction: A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. Methods: Two independent depressed samples: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier's predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Results: Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Conclusions: Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients. © 2013 Oliveira et al

Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

Pattern Classification of Working Memory Networks Reveals Differential Effects of Methylphenidate, Atomoxetine, and Placebo in Healthy Volunteers

Stimulant and non-stimulant drugs can reduce symptoms of attention deficit/hyperactivity disorder (ADHD). The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for ADHD treatment, but their differential effects on human brain function remain unclear. We combined event-related fMRI with multivariate pattern recognition to characterize the effects of MPH and ATX in healthy volunteers performing a rewarded working memory (WM) task. The effects of MPH and ATX on WM were strongly dependent on their behavioral context. During non-rewarded trials, only MPH could be discriminated from placebo (PLC), with MPH producing a similar activation pattern to reward. During rewarded trials both drugs produced the opposite effect to reward, that is, attenuating WM networks and enhancing task-related deactivations (TRDs) in regions consistent with the default mode network (DMN). The drugs could be directly discriminated during the delay component of rewarded trials: MPH produced greater activity in WM networks and ATX produced greater activity in the DMN. Our data provide evidence that: (1) MPH and ATX have prominent effects during rewarded WM in task-activated and -deactivated networks; (2) during the delay component of rewarded trials, MPH and ATX have opposing effects on activated and deactivated networks: MPH enhances TRDs more than ATX, whereas ATX attenuates WM networks more than MPH; and (3) MPH mimics reward during encoding. Thus, interactions between drug effects and motivational state are crucial in defining the effects of MPH and ATX

Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression

Currently, there are no neurobiological markers of clinical response for cognitive behavioural therapy (CBT) used in clinical practice. We investigated the neural pattern of activity to implicit processing of sad facial expressions as a predictive marker of clinical response. Sixteen medication-free patients in an acute episode of major depression underwent functional magnetic resonance imaging scans before treatment with CBT. Nine patients showed a full clinical response. The pattern of activity, which predicted clinical response, was analysed with support vector machine and leave-one-out cross-validation. The functional neuroanatomy of sad faces at the lowest and highest intensities identified patients, before the initiation of therapy, who had a full clinical response to CBT (sensitivity 71%, specificity 86%, P = 0.029)