Showing papers by "Mario F. Mendez published in 2023"

A Brain Mechanism for Hate.

Abstract: Back to table of contents Previous article Next article OpinionFull AccessA Brain Mechanism for HateMario F. Mendez, M.D., Ph.D.Mario F. MendezSearch for more papers by this author, M.D., Ph.D.Published Online:14 Feb 2023https://doi.org/10.1176/appi.neuropsych.20220121AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail Hate, defined here as intense dislike that encourages the elimination of others, involves dehumanization, or the denial of human qualities to others. Despite the importance of understanding the neuropsychiatry of hate, clinicians and investigators have devoted relatively little effort to its study. A review of the existing, albeit limited, literature suggests that hate depends on sufficiently dehumanizing others in order to permit their elimination. A potential brain mechanism for hate appears to involve an “animalistic” infrahumanization that results in withholding empathy from the devalued targets; this mechanism may be mediated by the inferior frontal cortex (IFC) (1). What follows is an attempt to support this hypothesis.As hate crimes, exemplified by horrific shootings at houses of worship and other public venues, continue to plague the United States, neurologists and psychiatrists may have an increasingly important role in elucidating the brain mechanisms that underlie hateful behavior. Although hatred is a major scourge of humankind, there is limited consensus on the nature of this phenomenon. There is disagreement as to whether hate is even an emotion. While some define hate as a deep, enduring, intense emotion (2), others consider it a long-lasting attitude or disposition of intense dislike punctuated by negative emotions such as anger (3). Whether emotion or disposition, there is additional disagreement on whether hate necessarily leads to an impulse to socially, psychologically, or physically eliminate its target. Nevertheless, hate that leads to negative impulses toward others is the source of much human tragedy.Given the controversy over the definition of hate, it is not surprising that there are a wide range of theories that attempt to explain hateful behavior. First, there are theories that reflect social psychological principles such as the need to belong and conform to social groups, pressures, norms, and rules. These social influences promote compliance with hateful behavior through deindividuation and group anonymity, diffusion of responsibility, passive compliance, blind obedience to authority, and ingroup versus outgroup distinctions (4). Second, some personality theories view certain people as being predisposed to hateful behavior, having a negative view of others, and possessing personality attributes that include authoritarianism and social dominance (5). These people homogenize all members of an outgroup and attribute deficient and negative character traits to them; thus, hateful individuals devalue and morally exclude outgroup members. Finally, there are direct theories of hate, such as Sternberg’s duplex theory (6). This theory proposes that hate involves a negation of intimacy, a negation of passion, and a negation of commitment, which are derived through stories that involve prototypes of targets with negative characteristics. The major feature that all of these theories seem to have in common is the need to dehumanize others, which is best seen as a cognitive heuristic that denies the human qualities of a hated “them.”The literature on dehumanization and its relation to the brain distinguishes two main forms of dehumanization: treating others as animals (devaluing through infrahumanization) and treating others as objects (a mechanistic unawareness of their basic humanity) (7). Investigators describe infrahumanization as the perception that outgroup individuals lack uniquely human traits, particularly secondary emotions, while retaining the basic emotions evident in other animals (8). Haslam’s dual model (7) further applies the term “animalistic dehumanization” to this devaluing of others as subhuman or nonhuman animals (e.g., “swine,” “monkeys,” and “vermin”), which arouses feelings of disgust and revulsion toward outgroups and makes harming them easier (9). Animalistic dehumanization normally occurs as a restraint on empathy when needed, such as situations involving social dominance or self-protection (e.g., averting one’s eyes from a panhandler) (10) and probably originates from activity in the ventrolateral prefrontal region—most probably the IFC (1). Haslam’s dual model further distinguishes a second form of dehumanization that results from a perceived lack of human nature or a perceived lack of being a human with an intentional mind (7, 11). This form of “mechanistic dehumanization,” which denies the existence of a mental life (“mentalization”) with thoughts, feelings, beliefs, and agency in others, sees those who are hated as nonliving machines or objects, resulting in feelings of indifference toward them (7). Mechanistic dehumanization is associated with decreased mesial prefrontal activity involving the ventromedial prefrontal cortex and perigenual anterior cingulate cortex (12) and decreased parietal and default mode network function (1).Taking the theories of hate and the studies on dehumanization into consideration, hate may depend on animalistic infrahumanization with heightened activity in the IFC facilitating disdain and disgust for the devalued target. The absence of moral emotional engagement, including hate, among frontolimbic brain-injured patients, such as those with frontotemporal dementia (FTD) (13), highlights the differential association of hate with animalistic dehumanization rather than with mechanistic dehumanization. Although the neuropathology of FTD is centered in the mesial frontal, anterior insula, and anterior temporal lobe structures (14)—regions purported to overlap with a proposed hate network (15)—patients with damage to these structures may have prominent mechanical dehumanization and indifference to others without the presence of hate.Actions based on hate continue to have grave consequences and require a more studied approach to cognitive factors, such as the type and origin of an underlying dehumanization that encourages harm toward others. Hate may emerge from a heightened normal mechanism for devaluing others, possibly mediated by the IFC, rather than the adulteration of mentalization seen with disease involving predominantly frontal dysfunction. Clearly this is a proposed starting point, or a hypothesis, for approaching an understanding of hate; hopefully, this hypothesis can stimulate further investigation of the neuropsychiatric mechanisms underlying hate, with an eventual goal of guiding efforts to mitigate its expression.Departments of Neurology and Psychiatry and Behavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles; Neurology Service, Neurobehavior Unit, VA Greater Los Angeles Healthcare System, Los Angeles.Send correspondence to Dr. Mendez ([email protected]).Dr. Mendez is supported by the National Institute on Aging (grant 1RF1AG050967).The author reports no financial relationships with commercial interests.References1. Bruneau E, Jacoby N, Kteily N, et al.: Denying humanity: the distinct neural correlates of blatant dehumanization. J Exp Psychol Gen 2018; 147:1078–1093Crossref, Medline, Google Scholar2. Reber AS, Reber E: The Penguin Dictionary of Psychology. New York, Penguin Books, 2002 Google Scholar3. Ekman P: Emotions Revealed. New York, Henry Holt and Company, 2003 Google Scholar4. Zimbardo PG: A situationist perspective on the psychology of evil: understanding how good people are transformed into perpetrators; in The Social Psychology of Good and Evil. Edited by Miller AG. New York, Guilford Press, 2004, pp 21–50 Google Scholar5. Altemeyer B: Highly dominating, highly authoritarian personalities. J Soc Psychol 2004; 144:421–447Crossref, Medline, Google Scholar6. Sternberg RJ, Sternberg K: The Nature of Hate. Cambridge, Cambridge University Press, 2008 Crossref, Google Scholar7. Haslam N: Dehumanization: an integrative review. Pers Soc Psychol Rev 2006; 10:252–264Crossref, Medline, Google Scholar8. Leyens J-P, Paladino PM, Rodriguez-Torres R, et al.: The emotional side of prejudice: the role of secondary emotions to ingroups and outgroups. Pers Soc Psychol Rev 2000; 4:186–197 Crossref, Google Scholar9. Goff PA, Eberhardt JL, Williams MJ, et al.: Not yet human: implicit knowledge, historical dehumanization, and contemporary consequences. J Pers Soc Psychol 2008; 94:292–306Crossref, Medline, Google Scholar10. Ligneul R, Girard R, Dreher J-C: Social brains and divides: the interplay between social dominance orientation and the neural sensitivity to hierarchical ranks. Sci Rep 2017; 7:45920Crossref, Medline, Google Scholar11. Harris LT, McClure SM, van den Bos W, et al.: Regions of the MPFC differentially tuned to social and nonsocial affective evaluation. Cogn Affect Behav Neurosci 2007; 7:309–316Crossref, Medline, Google Scholar12. Scalabrini A, Ebisch SJH, Huang Z, et al.: Spontaneous brain activity predicts task-evoked activity during animate versus inanimate touch. Cereb Cortex 2019; 29:4628–4645Crossref, Medline, Google Scholar13. Mendez MF, Anderson E, Shapira JS: An investigation of moral judgement in frontotemporal dementia. Cogn Behav Neurol 2005; 18:193–197Crossref, Medline, Google Scholar14. Seeley WW: Selective functional, regional, and neuronal vulnerability in frontotemporal dementia. Curr Opin Neurol 2008; 21:701–707Crossref, Medline, Google Scholar15. Zeki S, Romaya JP: Neural correlates of hate. PLoS One 2008; 3:e3556Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited byNone Metrics Keywordsdehumanizationpsychopathyfrontotemporal dementiaviolenceaggressionneuropsychologyPDF download History Received 21 June 2022 Revised 20 September 2022 Accepted 9 November 2022 Published online 14 February 2023

Network connectivity alterations across the MAPT mutation clinical spectrum.

Abstract: OBJECTIVE Microtubule-associated protein tau (MAPT) mutations cause frontotemporal lobar degeneration, and novel biomarkers are urgently needed for early disease detection. We used task-free fMRI mapping, a promising biomarker, to analyze network connectivity in symptomatic and presymptomatic MAPT mutation carriers. METHODS We compared cross-sectional fMRI data between 17 symptomatic and 39 presymptomatic carriers and 81 controls with: 1) seed-based analyses to examine connectivity within networks associated with the four most common MAPT-associated clinical syndromes (i.e., salience, corticobasal syndrome, progressive supranuclear palsy syndrome, and default mode networks), and 2) whole-brain connectivity analyses. We applied K-means clustering to explore connectivity heterogeneity in presymptomatic carriers at baseline. Neuropsychological measures, plasma neurofilament light chain, and gray matter volume were compared at baseline and longitudinally between the presymptomatic subgroups defined by their baseline whole-brain connectivity profiles. RESULTS Symptomatic and presymptomatic carriers had connectivity disruptions within MAPT-syndromic networks. Compared to controls, presymptomatic carriers showed regions of connectivity alterations with age. Two presymptomatic subgroups were identified by clustering analysis, exhibiting predominantly either whole-brain hypoconnectivity or hyperconnectivity at baseline. At baseline, these two presymptomatic subgroups did not differ in neuropsychological measures, though the hypoconnectivity subgroup had greater plasma neurofilament light chain levels than controls. Longitudinally, both subgroups showed visual memory decline (vs. controls), yet the subgroup with baseline hypoconnectivity also had worsening verbal memory and neuropsychiatric symptoms, and extensive bilateral mesial temporal gray matter decline. INTERPRETATION Network connectivity alterations arise as early as the presymptomatic phase. Future studies will determine whether presymptomatic carriers' baseline connectivity profiles predict symptomatic conversion. This article is protected by copyright. All rights reserved.

The Implications of Moral Neuroscience for Brain Disease: Review and Update.

Abstract: The last 2 decades have seen an explosion of neuroscience research on morality, with significant implications for brain disease. Many studies have proposed a neuromorality based on intuitive sentiments or emotions aimed at maintaining collaborative social groups. These moral emotions are normative, deontological, and action based, with a rapid evaluation of intentionality. The neuromoral circuitry interacts with the basic mechanisms of socioemotional cognition, including social perception, behavioral control, theory of mind, and social emotions such as empathy. Moral transgressions may result from primary disorders of moral intuitions, or they may be secondary moral impairments from disturbances in these other socioemotional cognitive mechanisms. The proposed neuromoral system for moral intuitions has its major hub in the ventromedial prefrontal cortex and engages other frontal regions as well as the anterior insulae, anterior temporal lobe structures, and right temporoparietal junction and adjacent posterior superior temporal sulcus. Brain diseases that affect these regions, such as behavioral variant frontotemporal dementia, may result in primary disturbances of moral behavior, including criminal behavior. Individuals with focal brain tumors and other lesions in the right temporal and medial frontal regions have committed moral violations. These transgressions can have social and legal consequences for the individuals and require increased awareness of neuromoral disturbances among such individuals with brain diseases.

A Transdisciplinary Program for Care of Veterans With Neurocognitive Disorders.

Abstract: Background Veterans face specific risk factors for neurocognitive disorders. Providing them with comprehensive care for dementia and related neurocognitive disorders is a challenge as the population ages. There is a need for family-centered interventions, specialized expertise, and collaboration among clinicians and caregivers. The literature suggests that application of a transdisciplinary care model can address these needs and provide effective dementia care. Observations The Veterans Affairs Greater Los Angeles Healthcare System has employed existing expertise to create a conference-centered transdisciplinary model that responds to the US Department of Veterans Affairs directive for a dementia system of care. This model involves direct participation of behavioral neurology, geriatric psychiatry, geriatrics, neuropsychology, nursing, and social work. In this model, the social worker serves as a dementia care manager and, along with the nurse specialist, assures long-term management through follow-up and monitoring. Transdisciplinary interactions occur in a clinical case conference where each discipline contributes to the veteran's care. The team generates a final report on treating these veterans, the caregiver's needs, referral for psychosocial services, and plans for monitoring and follow-up. Conclusions This model could be a template of a program for implementing the Dementia System of Care across Veteran Affairs medical centers.

Distinguishing Semantic Variant Primary Progressive Aphasia from Alzheimer’s Disease

Abstract: The differentiation of semantic variant primary progressive aphasia from dementia and Alzheimer’s disease can be difficult, particularly when the semantic anomia is pronounced. This report describes a patient who presented with complaints of memory loss and proved to have prominent semantic loss of all types of nouns, common and proper, concrete and abstract, yet continued to live independently and maintain his activities of daily living. The evaluation was consistent for semantic variant primary progressive aphasia with degradation of semantic knowledge and focal anterior temporal atrophy and hypometabolism. This report summarizes the literature and discusses the differential diagnosis of this disorder from Alzheimer’s disease and related dementias.

Plasma inflammation for predicting phenotypic conversion and clinical progression of autosomal dominant frontotemporal lobar degeneration

Abstract: Background Measuring systemic inflammatory markers may improve clinical prognosis and help identify targetable pathways for treatment in patients with autosomal dominant forms of frontotemporal lobar degeneration (FTLD). Methods We measured plasma concentrations of IL-6, TNFα and YKL-40 in pathogenic variant carriers (MAPT, C9orf72, GRN) and non-carrier family members enrolled in the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration consortium. We evaluated associations between baseline plasma inflammation and rate of clinical and neuroimaging changes (linear mixed effects models with standardised (z) outcomes). We compared inflammation between asymptomatic carriers who remained clinically normal (‘asymptomatic non-converters’) and those who became symptomatic (‘asymptomatic converters’) using area under the curve analyses. Discrimination accuracy was compared with that of plasma neurofilament light chain (NfL). Results We studied 394 participants (non-carriers=143, C9orf72=117, GRN=62, MAPT=72). In MAPT, higher TNFα was associated with faster functional decline (B=0.12 (0.02, 0.22), p=0.02) and temporal lobe atrophy. In C9orf72, higher TNFα was associated with faster functional decline (B=0.09 (0.03, 0.16), p=0.006) and cognitive decline (B=−0.16 (−0.22, −0.10), p<0.001), while higher IL-6 was associated with faster functional decline (B=0.12 (0.03, 0.21), p=0.01). TNFα was higher in asymptomatic converters than non-converters (β=0.29 (0.09, 0.48), p=0.004) and improved discriminability compared with plasma NfL alone (ΔR2=0.16, p=0.007; NfL: OR=1.4 (1.03, 1.9), p=0.03; TNFα: OR=7.7 (1.7, 31.7), p=0.007). Conclusions Systemic proinflammatory protein measurement, particularly TNFα, may improve clinical prognosis in autosomal dominant FTLD pathogenic variant carriers who are not yet exhibiting severe impairment. Integrating TNFα with markers of neuronal dysfunction like NfL could optimise detection of impending symptom conversion in asymptomatic pathogenic variant carriers and may help personalise therapeutic approaches.

Diagnostic Application of Automated Brain MRI Volumetry in Comparing Alzheimer Disease, Behavioral Variant Frontotemporal Dementia and Traumatic Brain Injury (P12-6.005)

Abstract:

Objective:

To evaluate if application of automated brain MRI volumetry can improve delineation of neurocognitive disorders.

Background:

Assessment of regional structural differences with brain MRI volumetric quantification can improve diagnostic separation of multiple neurodegenerative and non-neurodegenerative disorders.

Design/Methods:

We utilized 136 participants with traumatic brain injury (TBI; n=40), early (EOAD; n=45) and late onset Alzheimer disease (LOAD; n=31) and behavioral variant frontotemporal dementia (bvFTD; n=20). Participants had brain MRI including 3D T1 and analyzed with an FDA cleared software program, Neuroreader. At the time of analysis, Mini-Mental State Exam (MMSE) scores were available on majority of participants (n=125). One-way ANOVA compared mean age, MMSE, and brain volumes across all four groups with Bonferroni correction for multiple comparisons. Volumes were adjusted for total intracranial volume (TIV). Discriminant analysis was done with leave-one-out cross validation on measured brain volumes to determine diagnostic delineation. Automated linear regression identified predictive features.

Results:

LOAD was the oldest compared to other groups (p<.001). There were no statistically significant differences in sex (p=.58) with 54.4% women. EOAD and LOAD had the lowest MMSE scores compared to TBI (p=.04 for EOAD and p=.01 for LOAD). Regional volume difference across groups showed lowest hippocampal volumes in LOAD (p=.005), low white matter volume in TBI (p=.007), lower parietal lobe volumes in EOAD (p<.01), and lower frontal lobe volumes in bvFTD (p<.001). Regional volume differences resulted in a correct classification of 89%, 79.4% after cross validation. Predictive features included caudate, amygdala, frontal, parietal, temporal lobar and total white matter volumes.

Conclusions:

Lower parietal lobe volumes in EOAD and hippocampal volumes in LOAD as well as frontal atrophy of bvFTD reflect known anatomical distribution of pathology in those conditions. White matter volume loss in TBI has been noted with that condition. Brain MRI volumetry may be beneficial in clinical practice. Disclosure: Dr. Meysami has nothing to disclose. Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Brainreader ApS. Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Apollo Health . Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Neurevolution Medicine. Dr. Porter has nothing to disclose. David Merrill has nothing to disclose. Dr. Mendez has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Medical Education Speakers’ Bureau. Dr. Mendez has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for UpToDate. The institution of Dr. Mendez has received research support from NIH. Dr. Mendez has received publishing royalties from a publication relating to health care.

Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort.

Abstract: INTRODUCTION We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). METHODS Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). RESULTS Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. DISCUSSION Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD.

Learning slopes in early-onset Alzheimer's disease.

Abstract: OBJECTIVE Investigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity. A particular metric-the learning ratio (LR)-outperformed other learning slope calculations across analyses CONCLUSIONS: Learning slopes appear to be sensitive to early-onset dementias, even when controlling for the effect of total learning and cognitive severity. The LR may be the learning measure of choice for such analyses. HIGHLIGHTS Learning is impaired in amyloid-positive EOAD, beyond cognitive severity scores alone. Amyloid-positive EOAD participants perform worse on learning slopes than amyloid-negative participants. Learning ratio appears to be the learning metric of choice for EOAD participants.

Profiling baseline performance on the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort near the midpoint of data collection.

Abstract: OBJECTIVE The Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset <65 years), with the current study profiling baseline clinical, cognitive, biomarker, and genetic characteristics of the cohort nearing the data-collection mid-point. METHODS Data from 371 LEADS participants were compared based on diagnostic group classification (cognitively normal [n = 89], amyloid-positive EOAD [n = 212], and amyloid-negative early-onset non-Alzheimer's disease [EOnonAD; n = 70]). RESULTS Cognitive performance was worse for EOAD than other groups, and EOAD participants were apolipoprotein E (APOE) ε4 homozygotes at higher rates. An amnestic presentation was common among impaired participants (81%), with several clinical phenotypes present. LEADS participants generally consented at high rates to optional trial procedures. CONCLUSIONS We present the most comprehensive baseline characterization of sporadic EOAD in the United States to date. EOAD presents with widespread cognitive impairment within and across clinical phenotypes, with differences in APOE ε4 allele carrier status appearing to be relevant. HIGHLIGHTS Findings represent the most comprehensive baseline characterization of sporadic early-onset Alzheimer's disease (EOAD) to date. Cognitive impairment was widespread for EOAD participants and more severe than other groups. EOAD participants were homozygous apolipoprotein E (APOE) ε4 carriers at higher rates than the EOnonAD group. Amnestic presentation predominated in EOAD and EOnonAD participants, but other clinical phenotypes were present.