Forensic neurosciences

Sartori, Giuseppe; Pellegrini, Silvia; Mechelli, Andrea

doi:10.1097/wco.0b013e3283489754

Cited by 26 publications

(22 citation statements)

References 59 publications

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“…Nevertheless, such technology may help in a clinical setting by discriminating between those most difficult to categorize using traditional methods of assessment alone. Furthermore, it could potentially be used in a forensic setting as an objective means of reducing controversy in evaluations of mental illness and minimizing errors in detecting malingering (Sartori et al 2011). …”

Section: Discussionmentioning

confidence: 99%

Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

et al. 2013

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BackgroundGroup-level results suggest that relative to healthy controls (HCs), ultra-high-risk (UHR) and first-episode psychosis (FEP) subjects show alterations in neuroanatomy, neurofunction and cognition that may be mediated genetically. It is unclear, however, whether these groups can be differentiated at single-subject level, for instance using the machine learning analysis support vector machine (SVM). Here, we used a multimodal approach to examine the ability of structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor neuroimaging (DTI), genetic and cognitive data to differentiate between UHR, FEP and HC subjects at the single-subject level using SVM.MethodThree age- and gender-matched SVM paired comparison groups were created comprising 19, 19 and 15 subject pairs for FEP versus HC, UHR versus HC and FEP versus UHR, respectively. Genetic, sMRI, DTI, fMRI and cognitive data were obtained for each participant and the ability of each to discriminate subjects at the individual level in conjunction with SVM was tested.ResultsSuccessful classification accuracies (p < 0.05) comprised FEP versus HC (genotype, 67.86%; DTI, 65.79%; fMRI, 65.79% and 68.42%; cognitive data, 73.69%), UHR versus HC (sMRI, 68.42%; DTI, 65.79%), and FEP versus UHR (sMRI, 76.67%; fMRI, 73.33%; cognitive data, 66.67%).ConclusionsThe results suggest that FEP subjects are identifiable at the individual level using a range of biological and cognitive measures. Comparatively, only sMRI and DTI allowed discrimination of UHR from HC subjects. For the first time FEP and UHR subjects have been shown to be directly differentiable at the single-subject level using cognitive, sMRI and fMRI data. Preliminarily, the results support clinical development of SVM to help inform identification of FEP and UHR subjects, though future work is needed to provide enhanced levels of accuracy.

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Section: Discussionmentioning

confidence: 99%

Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

et al. 2013

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“…Such a tool could potentially be used in a clinical setting to inform the clinical evaluation of those difficult to categorize using traditional methods of clinical assessment alone (Lanius et al 2007; Pace & Heim, 2011). In addition, it could potentially be used in a forensic setting as an objective means of reducing controversy in evaluations of mental illness and minimizing errors in detecting malingering (Rosen & Taylor, 2007; Sartori et al 2011). In contrast, no significant regions were detected for the comparison between survivors with and without PTSD using standard mass-univariate techniques in which each voxel is considered as a spatially independent unit.…”

Section: Discussionmentioning

confidence: 99%

“…It follows that the application of SVM to neuroimaging data can only reach the same level of diagnostic accuracy as traditional methods of clinical assessment. Nevertheless, such application could still be useful as an objective means of reducing controversy in forensic evaluations of mental insanity and minimizing errors in detecting malingering (Rosen & Taylor, 2007; Sartori et al 2011). For a more comprehensive discussion of the limitations of the use of SVM in psychiatry and neurology, see our recent review (Orrù et al 2012).…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it can be difficult to discern the symptoms and obtain an accurate diagnosis. In addition, cases of pseudo-PTSD, in which a patient's presentation is but a simulation of the actual syndrome, can be particularly difficult to identify due to the traditional assumption that a patient's reported symptoms can be accepted as valid (Rosen & Taylor, 2007; Sartori et al 2011). A number of neuroimaging studies have therefore compared individuals who did and did not develop PTSD following exposure to a traumatic event, in order to identify possible diagnostic biomarkers (Shin et al 2006; Nardo et al 2010; Robinson & Shergill, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level

et al. 2013

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BackgroundAt present there are no objective, biological markers that can be used to reliably identify individuals with post-traumatic stress disorder (PTSD). This study assessed the diagnostic potential of structural magnetic resonance imaging (sMRI) for identifying trauma-exposed individuals with and without PTSD.MethodsMRI scans were acquired from 50 survivors of the Sichuan earthquake of 2008 who had developed PTSD, 50 survivors who had not developed PTSD and 40 healthy controls who had not been exposed to the earthquake. Support vector machine (SVM), a multivariate pattern recognition technique, was used to develop an algorithm that distinguished between the three groups at an individual level. The accuracy of the algorithm and its statistical significance were estimated using leave-one-out cross-validation and permutation testing.ResultsWhen survivors with PTSD were compared against healthy controls, both grey and white matter allowed discrimination with an accuracy of 91% (p < 0.001). When survivors without PTSD were compared against healthy controls, the two groups could be discriminated with accuracies of 76% (p < 0.001) and 85% (p < 0.001) based on grey and white matter, respectively. Finally, when survivors with and without PTSD were compared directly, grey matter allowed discrimination with an accuracy of 67% (p < 0.001); in contrast the two groups could not be distinguished based on white matter.ConclusionsThese results reveal patterns of neuroanatomical alterations that could be used to inform the identification of trauma survivors with and without PTSD at the individual level, and provide preliminary support to the development of SVM as a clinically useful diagnostic aid.

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“…Neuroimaging data could help psychiatric assessment in forensic settings when used in combination with standard clinical measures [86] and past psychiatric interviews.…”

Section: Discussionmentioning

confidence: 99%

Functional Neuroimaging in Psychopathy

et al. 2015

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Background and Aim: Psychopathy is associated with cognitive and affective deficits causing disruptive, harmful and selfish behaviour. These have considerable societal costs due to recurrent crime and property damage. A better understanding of the neurobiological bases of psychopathy could improve therapeutic interventions, reducing the related social costs. To analyse the major functional neural correlates of psychopathy, we reviewed functional neuroimaging studies conducted on persons with this condition. Methods: We searched the PubMed database for papers dealing with functional neuroimaging and psychopathy, with a specific focus on how neural functional changes may correlate with task performances and human behaviour. Results: Psychopathy-related behavioural disorders consistently correlated with dysfunctions in brain areas of the orbitofrontal-limbic (emotional processing and somatic reaction to emotions; behavioural planning and responsibility taking), anterior cingulate-orbitofrontal (correct assignment of emotional valence to social stimuli; violent/aggressive behaviour and challenging attitude) and prefrontal-temporal-limbic (emotional stimuli processing/response) networks. Dysfunctional areas more consistently included the inferior frontal, orbitofrontal, dorsolateral prefrontal, ventromedial prefrontal, temporal (mainly the superior temporal sulcus) and cingulated cortices, the insula, amygdala, ventral striatum and other basal ganglia. Conclusions: Emotional processing and learning, and several social and affective decision-making functions are impaired in psychopathy, which correlates with specific changes in neural functions.

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Forensic neurosciences

Abstract: Genetic and neuroimaging techniques may provide information which, when considered in combination with other sources of evidence, might prove useful in advancing knowledge about mens rea.

Cited by 26 publications

References 59 publications

Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level

Using structural neuroanatomy to identify trauma survivors with and without post-traumatic stress disorder at the individual level

Functional Neuroimaging in Psychopathy

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