Brain & Behaviour Week 10: Substance Abuse - Lecture Notes PDF

Brain & Behaviour PSYC11212 Week 10 Substance Abuse Dr Annie Pye [email protected] Brain & Behaviour PSYC11212 Week 10 Substance Abuse Dr Annie Pye [email protected] Looking after yourself • This lecture will necessarily engage with topics that can be emotional. • Some people might feel that the content of this lecture is “close to home” • Do whatever works for you in terms of looking after your wellbeing e.g. take a 5 - minute break at any point. • Feel free to approach me (Annie), your academic advisor/year tutor, or the University well - being services (e.g. the University Counselling Service) This week • Substance Abuse Disorders – Reinforcement – Craving and relapse – Different types of substances and their effects (Opiates, Stimulants, Nicotine, Alcohol, and Cannabis) – Hereditary elements – Therapies for substance abuse Substance Abuse Disorder • Substance abuse is a pattern of drug use in which people rely on a drug chronically and excessively and not for therapeutic reasons • Addiction or dependence refers to being physically dependent on a drug in addition to abusing it • Can pose a serious threat • Cocaine – psychotic behaviour, brain damage, death • Designer drugs – Untested, potentially contaminated. E.g. synthetic opiate tainted with a neurotoxin • Intravenous drugs – risk of contracting infectious diseases, overdose and death, harm caused to individual’s life, loved ones and society • Alcohol – cirrhosis of the liver, increased risk of heart disease and stroke, Korakoff ’s syndrome • Smoking – increased risk of many cancers, heart disease, stroke etc. So why take drugs?Substance Abuse Disorder Why take drugs? • All of these are addictive substances • Taking certain substances results in positive reinforcement Positive reinforcement = the addition of a reinforcing stimulus following a behaviour that makes it more likely that the behaviour will occur again in the future Positive Reinforcement in Drug Use • Reinforcing stimuli have a greater effect if it occurs immediately after the behaviour • Drug users prefer heroin to morphine as heroin has a more rapid effect – it is more lipid soluble Positive Reinforcement: Neural Mechanisms • Triggers the release of dopamine in the nucleus accumbens (NAC) • Process of addiction begins in the mesolimbic dopaminergic system • Produce long term changes in other brain regions – starting with the ventral tegmental area (VTA ) • Saal et al. (2003) found that there was increased strength of the excitatory synapses on dopaminergic neurons in the VTA of mice after a single administration of an addictive drug Positive Reinforcement: Neural Mechanisms • Changes in the VTA lead to increased activation in a variety of regions that receive dopaminergic input from the VTA. • Synaptic changes that are responsible for the compulsive behaviours that characterise addiction occur only after continued use. • I mportant changes occur in the dorsal striatum which is part of the basal ganglia • The basal ganglia plays a critical role in instrumental conditioning. Negative reinforcement • Not a punishment rather the removal of something unpleasant • Tolerance – decreased sensitivity from continued use • Withdrawal symptoms – generally the opposite of the drug itself – t he body may have started to compensate for the disturbed homeostatic mechanisms Negative Reinforcement • Potentially maintains addiction – Withdrawal symptoms are unpleasant, taking the drug removes them, producing negative reinforcement • Explanation for start of addiction in some scenarios – Taking a drug to deal with stress or other problems Craving & Relapse • Cravings can occur after a long period of abstinence. • Potentially due to long - lasting brain changes • Drug - related stimuli can elicit classically conditioned responses in substance abusers, both physiologically and subjectively - cravings • Franken (2003) suggests craving and relapse are due to ‘attentional bias’ – cued by cognitive processes and increases in dopamine in response to drug stimuli. Craving & Relapse • Franken’s (2003) review indicated dopamine increases in the nucleus accumbens (among other areas) in response to drug - related stimuli • Volkow et al. (2006) used imaging to demonstrate that dopamine increased in relation to cocaine - cues in the dorsal striatum but not the ventral striatum (where the nucleus accumbens is located) • Dopamine release is important in the positive reinforcement of drug use, also playing a role in craving and relapse Craving & Relapse • Prefrontal cortex has also been implicated (see Carlson; Godlstein & Volkow , 2011) • But do abnormalities in the connectivity or structure of the PFC predispose people to substance abuse or does substance abuse causes these abnormalities? • Either way, PFC plays an important role through emotion regulation and inhibitory control (Goldstein & Volkow , 2011) Opiates • Heroin is the most commonly abused opiate and abuse comes with high personal and societal costs. – Tolerance means the person will have to take more and more of the drug to achieve a high – Needle use – Transmission to unborn child – Uncertainty of strength and what it can be mixed with Opiates • Systematic administration of opiates stimulates opiate receptors – Analgesia (Periaqueductal grey matter) – Hypothermia ( Preoptic area) – Sedation ( Mesencephalic reticular formation) – Reinforcement (Ventral tegmental area and nucleus accumbens ) Opiates • Opiate related stimuli trigger the release of dopamine in the nucleus accumbens (NAC) – Wise et al. (1995) – increases of 150 - 300% in levels of dopamine in rats’ NAC while the rat pressed a lever that delivered heroin – Rats will also press a lever to inject opiates into the NAC or the ventral tegmental area • Suggesting that the reinforcing effects of opiates are produced by the activation of neurons of the mesolimbic system and release of dopamine in the NAC Stimulant Dugs: Cocaine and Amphetamine • Cocaine and amphetamines have similar behavioural effects but their sites of action are different • Cocaine – deactivates dopamine transporter proteins, blocking the reuptake of dopamine • Amphetamine – also inhibits the reuptake of dopamine but directly stimulates the release of dopamine from terminal buttons as well • Likely highly addictive – Bozath & Wise (1985) reported that rats that self - administered cocaine were 3 times more likely to die than rats who self - administered heroin • Blocking dopamine receptors or destroying dopaminergic terminals in the NAC causes cocaine and amphetamines to lose much of their reinforcing effect (McGregor & Roberts, 1993; Caine et al., 1995; Caine & Koob , 1994) Highlights the role of the mesolimbic dopamine system in reinforcementStimulant Dugs: Cocaine and Amphetamine Nicotine • Smoking is the leading cause of preventable death in countries such as the UK and US (Pirie et al., 2013) • Despite decreases in the prevalence of smoking, the risk of smoking - related deaths has increased over the last 50 years ( Thun et al., 2013) • Why do people continue to smoke? • Evidence that it is highly addictive, e.g. continuing to smoke after a heart attack, cancer surgery etc. Nicotine • Animals will also self - administer nicotine • Smoking stimulates nicotinic acetylcholine receptors • Nicotine is associated with the release of dopamine in the NAC, reinforcing the behaviour ( Jasinka et al., 2014) Nicotine and Dopamine Release in the Nucleus Accumbens . The graph shows changes in dopamine concentration in the nucleus accumbens , measured by microdialysis , in response to injections of nicotine or saline. The arrows indicate the time of the injections. Adapted from Damsma , G., Day, J., and Fibiger, H. C. European Journal of Pharmacology , 1989, 168 , 363 – 368. Nicotine • It has also been noted that damage to the insula disrupts smoking addiction • Naqvi et al. (2007) – 19 smokers with insula damage following acquired brain injury – 50 smokers with no insula damage following acquired brain injury – No difference in whether or not they had quit when they participated in the study – However, those who had insular damage were significantly more likely to have a disruption of smoking addiction Damage to the Insula and Smoking Cessation. The diagrams show the regions of the brain (shown in red) where damage was most highly correlated with cessation of smoking. From Naqvi , N. H., Rudrauf , D., Damasio , H., and Bechara , A. Science , 2007, 315 , 531 – 534. Copyright © 2007. Reprinted with permission from AAAS. Alcohol • Potential effects – Mild euphoria – Anxiolytic: reduces the discomfort of anxiety – Disinhibition – Alcohol myopia (Steel & Josephs, 1990; MacDonald et al., 1998) • tendency for people to respond to near and immediate cues while ignoring more remote cues and potential consequences Alcohol • Increases activity in the dopaminergic neurons of the mesolimbic system • Two major sites of action – Indirect antagonist at NMDA receptors – Indirect agonist at GABA A receptors (anxiolytic and sedative effects) Alcohol • Increased sensitivity of NMDA receptors after suppressive effect of alcohol is removed can trigger seizures and convulsions • Drugs which block NMDA receptors were shown to prevent the seizures in mice ( Liljequist , 1991) Alcohol • The reinforcing effect of alcohol is not solely due to the dopaminergic system • Alcohol can also trigger the release of endogenous opioids • Several studies have shown that drugs that block opiate receptors also block the reinforcing effects of alcohol in a variety of species, including rats, monkeys, and humans (Carlson) • The level of opioid receptors increases with abstinence and is thought to be related to cravings for alcohol Craving for Alcohol and μ Opiate Receptors. The PET scans show the presence of μ opiate receptors in the dorsal striatum of detoxified alcoholic patients and healthy control subjects. The graph shows the relative alcohol craving score as a function of relative numbers of μ opiate receptors. Scans and data points from Heinz, A., Reimold , M., Wrase , J., et al. Archives of General Psychiatry, 2005, 62, 57 – 64. By permission. Case study Name: Jimmie G Admitted to a home for the elderly aged 49 Occupation: Served in the Navy Doc: How old are you? Patient: 19 Korsakoff syndrome • Often seen in alcoholics who are malnourished • Caused by a lack of vitamin B1 in the brain and exacerbated by the toxic effects of alcohol • Damage to areas of the thalamus and the mammillary bodies - structures important for encoding new memories. Cannabis • Tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis • Cannabinoid Type 1 (CB1) receptors mediate most of the psychotropic effects of THC • Blocking CB1 receptors abolishes the high produced by smoking cannabis ( Huestis et al., 2001) • THC also has a stimulating effect on dopaminergic neurons THC and Dopamine Secretion in the Nucleus Accumbens . The graph shows changes in dopamine concentration in the nucleus accumbens , measured by microdialysis , in response to injections of THC or an inert placebo. Adapted from Chen, J., Paredes , W., Li, J., et al. Psychopharmacology , 1990, 102 , 156 – 162. Cannabis • CB1 receptors also have a probable role in the reinforcing effects of other drugs as well as cannabis – Blocking CB1 receptors in mice can abolish the reinforcing effect of cannabis, morphine, and heroin ( Cossu et al., 2001) and reduce the reinforcing effects of alcohol ( Houchi et al., 2005) – Rimonabant, a drug which blocks CB1 receptors, decreases the reinforcing effects of nicotine Heredity and Drug Abuse • Why can some people use drugs and not become dependent? • Genetic and environmental factors influence whether someone is likely to take the substance in the first place and their likelihood of becoming dependent • There are both general factors and drug specific factors Heredity and Drug Abuse • Kendler et al. (2003) investigated the specificity of genetic and environmental risk factors for use and abuse/dependence of 6 classes of illicit substances – Interviews of 1,196 male - male twin pairs – Found that environment plays a stronger role in drug use but genetics play a stronger role in determining whether the person becomes addicted Heredity of addiction • It is estimated that 40 - 60% of the vulnerability to addiction can be attributed to genetic factors • Includes both variability in metabolism of the drug and variability in the sensitivity to the reinforcing effects • Environmental factors also influence addiction – Drug availability, low SES, poor parental support, stress Volkow & Li (2005) Therapy for Drug Abuse • Personal and societal costs of drug abuse means effective treatments are important • Opiate addiction is most commonly treated with methadone, an orally administered replacement drug • A newer drug, buprenorphine blocks the effect of opiates and produces only a weak opiate effect. Buprenorphine as a Treatment for Opiate Addiction. The graph shows the effects of treatment with buprenorphine, buprenorphine + naloxone, and a placebo on opiate craving in recovering opiate addicts. Adapted from Fudala , P. J., Bridge, T. P., Herbert, S., et al. New England Journal of Medicine , 2003, 349 , 949 – 958. Therapy for Drug A buse • Immunotherapy – vaccines specific to the substance abused • Deep brain s timulation (DBS) – DBS of the NAC has had some promising effects, however it is a high risk procedure. • Transcranial magnetic stimulation (TMS) – TMS is less invasive and has shown efficacy in reducing tobacco use but the effects on nicotine use diminished over time. Summary • Positive and negative reinforcement • Craving and relapse • Opiates (mainly heroin), stimulant drugs (cocaine and amphetamine), nicotine, alcohol, and cannabis. • Heredity and drug abuse • Therapy for drug abuse Reading • Carlson • In chapter ‘Autistic, Attention Deficit, Stress, and Substance Abuse Disorders’ • Pp. 207 - 221 References Franken, I. H. (2003). Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Progress in Neuro - Psychopharmacology and Biological Psychiatry, 27 (4), 563 - 579. Goldstein, R. Z., & Volkow , N. D. (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12 (11), 652 - 669. MacDonald, T. K., Zanna , M. P., & Fong, G. T. (1998). Alcohol and intentions to behave in risky health - related behaviours: Experimental evidence for a causal relationship. In Adair, J. & Craiks , F. (Eds.), Advances in Physiological Science: Vol. 2, Developmental, personal, and Social Aspects (pp. 12 - 59). UK: Psychology Press Pirie, K., Peto , R., Reeves, G. K., Green, J., Beral , V., & Million Women Study Collaborators. (2013). The 21st century hazards of smoking and benefits of stopping: a prospective study of one million women in the UK. The Lancet, 381 (9861), 133 - 141. Steele, C. M., & Josephs, R. A. (1990). Alcohol myopia: Its prized and dangerous effects. American Psychologist, 45(8), 921. Thun , M. J., Carter, B. D., Feskanich , D., Freedman, N. D., Prentice, R., Lopez, A. D., ... & Gapstur , S. M. (2013). 50 - year trends in smoking - related mortality in the United States. New England Journal of Medicine, 368 (4), 351 - 364. Volkow , N., & Li, T. K. (2005). The neuroscience of addiction. Nature neuroscience, 8 (11), 1429 - 1430. Volkow , N. D., Wang, G. J., Telang , F., Fowler, J. S., Logan, J., Childress, A. R., ... & Wong, C. (2006). Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. The Journal of Neuroscience, 26 (24), 6583 - 6588. AutismBrain and Behaviour –PSYC11212 & 11222Dr Amber RuigrokMonday 17 April 2023 Looking after yourself •This lecture will necessarily engage with topics that can be emotional (including links to interviews with people describing in their own words what it is like to be autistic) • Some people may feel that the content of this lecture is “too close to home” • Do whatever works for you in terms of looking after your wellbeing, e.g., take a 5 -minutes break at any point • Feel free to approach me (Amber), your academic advisor/year tutor, or University wellbeing services NEURODIVERSITY, MODELS AND LANGUAGE Neurodiversity “A biological truism that refers to the limitless variability of human nervous systems on the planet, in which no two can ever be exactly alike” –Judy Singer - An advocacy term to name the Neurodiversity Movement, a civil rights movement for psycho -medically labelled minorities and their allies - Neurocognitive difference as a minority class / a diversity of minds https://neurodiversity2.blogspot.com/p/ what.html Shifting paradigms of disability https://www.ombudsman.org.uk /sites/default/files/FDN-218144_Introduction_to_the_Social_and_Medical_Models_of_Disability.pdf; https:// aaspire.org/inclusion-toolkit/neurodiversity/ Medical model of disability •Impairment first • Impairment is the cause of being unable to access goods/services or participate in society • Focuses of fixes or services specific for their ‘problems’ • Over -focusing on what the person cannot do instead of what they can do. https:// www.ombudsman.org.uk /sites/default/files/FDN-218144_Introduction_to_the_Social_and_Medical_Models_of_Disability.pdf The impairment is the problem W--r?kFienkUg ‘faok?i’F’ s?a-ikUg ’-feeU’ Tu’ se-ikUg ’aoti-a’ sacoackFa g ’aoti-a’ NfiU g ataUe?manFg Fakm s?a-ikUg Fokn’?eoF srocaen’ s?aa-fg Ffaok?i’F’ s?a-ikUi’Fg Fokinincg -anFoa’ LanaDiF’ sfaUFaoa g EeoC’fe?’ I r-kFienkUg u’A-feUeci’F’ Social model of disability •Preferred model • Created by disabled people • Seeks to remove barriers to allow disabled people to participate in society • Differentiates between impairment and disability • Do not ‘have’ a disability but a disability is experienced • Autism is a different ‘way -of -being’ • Autistic people are being excluded from society for behaving differently from the ‘norm’ https:// www.ombudsman.org.uk /sites/default/files/FDN-218144_Introduction_to_the_Social_and_Medical_Models_of_Disability.pdf Factors within society cause disability ”LaUiaD–ging wa i-kUgwe aU uetaoFA Rk-CgeDgr’aDrUg a r-kFien Snk--a’’ibUag antioenmanF sacoackFa g ’aoti-a’ hi’-oiminkFieng ing am?UeAmanF Taa ’gneFg knFi-i?kFa Snk--a’’ibUag Fokn’?eoF uoaUr i-a Snk--a’’ibUaginDeomkFien ha PtkUrinc p’’rm?Fien’ Collaborations with the Autistic Community •Acceptance of autistic people • • https:// aaspire.org /inclusion-toolkit/participatory -research/ Collaborations with the Autistic Community •Acceptance of autistic people • Autistic- partnered research collaboratives –Autism @ Manchester • Research co- designed by autistic people has more impact “Nothing about us without us” https:// aaspire.org /inclusion-toolkit/participatory -research/ Community-preferred language Kenny, (2016) Autism, doi: 10.1177/1362361315588200 Identity -first language Percentage of participants within each group preferring oneof the following terms to communicate about autism. Difference not deficit “Neurodiversity , short for neurological diversity, refers to the diversity of human brains and minds, and to the idea that this is a natural, valuable form of diversity.” Reducing stigma Ableistlanguage refers to language that assumes disabled people are inferior to nondisabled people. • Strategies for avoiding ableist language: – Special interests vs areas of interest / expertise or focused interests – Autism symptoms vs specific autistic characteristics, features or traits – Suffer vs impact or affect Bottema -Beutel , (2021) Autism in Adulthood, doi: 10.1089/aut.2020.0014 What are some of influences that have changed how society understands autistic people? Disability Models Autistic Community application of involvement of Medical Model Social Model preferred informed legislation “Nothing about us without us” Autistic community involvement Impairment Factors within society disability defined by Collaborative research practices increased Via, e.g., Neurodiversity AUTISM Autism Prevalence Autism diagnostic criteria and process Diagnostic criteria as per the Diagnostic and Statistical manual of Mental Disorders (DSM -5) APA, 2013 Autism Spectrum Disorder: - Persistent difficulties in social communication and interaction - Restricted, repetitive patterns of behaviour, interests, or activities - Sensory hyper -or hypo -sensitivities Diagnostic process usually includes standardized diagnostic interviews with person and/or caregivers, and possibly observation of person in various settings (home, school, clinic) • Symptoms must be present in the early developmental period (but may not become fully manifest until social demands exceed limited capacities, or may be masked by learned strategies in later life). • Symptoms cause clinically significant impairment in social, occupational, or other important areas of current functioning. • Not better explained by intellectual disability Autism: - Differences in social communication and interaction - Specific patterns of behaviour, passionate interests, or focused activities - Sensory hyper -or hypo -sensitivities Autism spectrum? e.g., high-functioning vs low - functioning Underlying biological mechanismsWhat are some of the biological underlying mechanisms that may lead to a behaviouralrepresentation that can be diagnosed as autism?Genetic causes of autismTw i n s : MZ rates: 60%, DZ rates: 5%;Siblings: Broader autism phenotype: 25%“If you have met one autistic person, you have met one autistic person” Underlying biological mechanismshttp://autism.mindspec.org/autdb/Welcome.doEnvironmentEnvironment“If you have met one autistic person, you have met one autistic person” Underlying biological mechanisms“If you have met one autistic person, you have met one autistic person”Mega-analysis of brain differences between autistic (1,571) people and non-autistic (1,651) people•Smaller subcortical volumes of the pallidum, putamen, amygdala and nucleus accumbens(Cohen’s d = 0.13 to -0.13)•Increased cortical thickness in the frontal cortex and decreased thickness in the temporal cortex (Cohen’s d = -0.21 to 0.20)•No age-specific differences found between the groupsVan Rooijet al., (2018) American Journal of Psychiatry, doi: 10.1176/appi.ajp.2017.17010100 Neurodevelopmental conditionEnvironmentEnvironment“If you have met one autistic person, you have met one autistic person”•Interactions with the environment•Not limited to childhood•Different life stages can bring new challenges but also advantages Co-occurring mental health and other conditions Articles 824 www.thelancet.com/psychiatry Vol 6 October 2019 (QE p<0·0001). Additionally, prevalence estimates of most co-occurring mental health conditions (with the exception of disruptive, impulse-control, and conduct disorders) were much higher than in the general population, as reported in previous representative studies (post hoc). 46–53 We d i d u n i v a r i a b l e ( table 2; appendix pp 36–75) and multivariable (table 3) meta-regressions for each co- occurring mental health condition. Across studies, older age was associated with lower prevalence of ADHD and higher prevalence of depressive, bipolar, and schizo- phrenia spectrum disor ders than younger age was. Studies with a high proportion of female participants reported higher prevalence of depressive disorders than those with a low proportion of female participants did. Studies with a high proportion of people with intellectual disability reported higher prevalence of schizophrenia spectrum disorders than those with a low proportion of people with intellectual disability did. Studies from countries with higher HDI reported lower prevalence of OCD than those from countries with a lower HDI. In the studies not included in the meta-analyses (appendix p 22), for trauma and stressor-related disorders, post-traumatic stress disorder was most commonly reported, with prevalence estimates ranging 0–3·6%. For substance-related and addictive disorders, alcohol abuse or dependence were most commonly reported, with prevalence estimates ranging 0–11%. Gender dys phoria was reported in none of the studies identiﬁed for the qualitative synthesis. Finally, we carried out a series of sensitivity analyses. Sensitivity analyses using the normal-binomial model for overall pooled estimates yielded similar results to those of the main model (ie, 95% CIs substantially overlapped, I ! p oint estimates were similar, and point estimates of prevalence were 2–4% lower using the normal-binomial model than when using the random-e" ects model and double arcsine transformation in our primary analyses; appendix p 110). For all co-occurring mental health conditions (signiﬁcant only for anxiety disorders; dis- ruptive, impulse-control, and conduct disorders; and Number of datapoints in m eta-analysis* Autism population sample size (n) Autism population General population prevalence (95% CI or SE) Subgroup moderator analysis Pooled prevalence (95% CI; 9 5% PI) I! (95% C I; p value†) Prevalence in population or registry-based studies (95% CI; 95% PI) Prevalence in clinical sample-based studies (95% CI; 9 5% PI) R! (QE p value) I! (95% C I) QM p value Attention-deﬁcit hyperactivity disorder 89 210 249 28% (25–32; 4–63) 99·65% (99·55–99·85; <0·0001) 7·2% (6·7–7·8; point prevalence, aged "18 y ears) 46 22% (17–26; 1–55) 34% (29–39; 7–69) 2·05% (<0·0001) 99·64% (99·60–99·84) 0·0004 Anxiety disorders 68169 829 20% (17–23; 2–48) 99·53% (99·42–99·87; <0·0001) 7·3% (4·8–10·9; current prevalence, across ages) 47 15% (11–19; 0·5–42) 26% (22–31; 1–56) 0% (<0·0001) 99·54% (99·20–99·85) 0·0002 Depressive disorders 65162 671 11% (9–13; 0–33) 99·41% (99·39–99·81; <0·0001) 4·7% (4·4–5·0; point prevalence of MDD , across ages) 48 8% (5–11; 0·01–28) 14% (11–18; 1–38) 0·23% (<0·0001) 99·40% (99·37–99·80) 0·0003 Bipolar and related disorders 38 153 192 5% (3–6; 0–19) 99·50% (99·40–99·82; <0·0001) 0·71% (0·56–0·86) for bipolar I; and 0·50% (0·35–0·64) for bipolar II (1-year prevalence, across ages) 49 3% (2–5; 0–16) 7% (4–10; 0–24) 0·35% (<0·0001) 99·50% (99·48–99·81) 0·018 Schizophrenia spectrum and psychotic disorders 42 166 627 4% (3–5; 0–14) 99·18% (99·00–99·87; <0·0001) 0·46% (0·41–0·50; 1-y ear prevalence, across ages) 50 2% (1–4; 0–11) 7% (4–9; 0–19) 0% (<0·0001) 99·18% (99·01–99·84) 0·0004 Obsessive-compulsive and related disorders 47 53 243 9% (7–10; 1–21) 96·85% (96·75–99·87; <0·0001) 0·7% (0·4–1·1; 1-y ear prevalence, aged #18 years) 51 4% (2–6; 0–13) 12% (10–15; 3–26) 12·51% (<0·0001) 96·20% (96·17–99·37) <0·0001 Disruptive, impulse-control, and conduct disorders 50 140 946 12% (10–15; 0–36) 99·52% (99·47–99·90; <0·0001) 8·9% (SE 0·5; 1-ye ar prevalence, aged #18 years) 52 7% (4–10; 0–28) 22% (17–27; 3–50) 0% (<0·0001) 99·53% (99·42–99·88) <0·0001 Sleep–wake disorders 26190 963 13% (9–17; 0–43) 99·87% (99·78–99·93; <0·0001) 3·7% (NA; 1-y ear prevalence, aged "18 years) 53 11% (7–17; 0–39) 16% (8–25; 0–47) 8·52% (<0·0001) 99·85% (99·77–99·91) 0·356 General population prevalence estimates were selected from latest meta-analyses or large-scale population-based studies, as cited. R! is the proportion of true heterogeneity that can be explained by the moderator, the QE statistic and its p value show the signiﬁcance of residual heterogeneity that is unaccounted for by the moderator, and the QM statistic and its p value show whether the moderator is statistically signiﬁcant in explaining heterogeneity. PI=prediction interval. MDD=major depressive disorder. NA=not available. *Number of datapoints extracted from studies reporting the co-occurring condition. †Cochran’s Q test p value. Table !: Pooled estimates of prevalence of co-occurring mental health and psychiatric conditions in autism and general population and moderator analy\ sis by study design Lai, (2019) The Lancet, doi: 10.1016/S2215 -0366(19)30289 -5 Co -occurring diagnoses are common in people with autism across the lifespan, e.g.: • ADHD (28%) • Anxiety (20%) • Depression (11%) Why might someone be diagnosed with Autism? such as spectrum? Autism -related behavioural features Differences in social communication & interactions Specific behaviours or areas of interest Diagnostic process identifying features Every person has a unique set of autistic features Behaviour Cognition Brain Genes E nv i r o n m e n t Dev e l o p m e n t via a via interactive relationships May have co- occurring mental health conditions SEX AND/OR GENDER DIFFERENCES Sex vs Gender “Gender”= encompasses experiential, social and cultural components including gender related norms, roles, interests, expressions and identity “Sex” = a biological construct often defined by biological and physical characteristics, including sex -related chromosomes http:// www.who.int /mediacentre /factsheets/fs403/ en/ccc In real life, sex and gender are often interrelated and interactive Sex differences Werling& Geschwind , (2013) Current Opinion in Neurology, doi: 10.1097/WCO.0b013e32835ee548 Suggested possible reasons: • Genetic differences and susceptibility Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction \ of this article is prohibited. CE: Alpana; WCO/19301; Total nos of Pages: 8; WCO 19301 A recent study from the United Kingdom addressed this potential diagnostic gap by character- izing children with high autistic traits who met or fell short of the threshold for ASD diagnosis [30 && ]. A significantly smaller proportion of high-scoring girls met full ASD diagnostic criteria than males (38 vs. 56%), whereas ASD-diagnosed girls had a higher mean total problem score (hyperactivity, anxiety, and conduct, peer, and prosocial problems) and a higher frequency of low IQ than ASD- diagnosed boys. Girls without diagnoses showed increased communication difficulties, but reduced social impairments as compared to nondiagnosed boys. Thus, it may be that relatively higher levels of social ability in females preclude full diagnosis of ASD, particularly for those who are high- functioning. Nevertheless, whether the male-skewed prevalence of ASD is due to biased diagnosis of sex-differential presentations of the disease or to true sex differences in prevalence (or both), sex-specific biology is likely to play a role. For the remainder of this review, we discuss the relationships between ASD and the two major drivers of sex-specific biology: genetics and hormones. SEX DIFFERENCES IN GENETIC CONTRIBUTIONS TO AUTISM SPECTRUM DISORDER RISK Biological theories for the sex difference in ASD prevalence most frequently take the form of a multiple-threshold multifactorial liability model [31], in which females have a higher threshold for reaching affectation status than males (Fig. 1a). Increasing genetic liability for ASDs Minimum genetic liability sufficient to cause ASD Proportion of population Proportion of population Population mean Affected individuals Increasing total liability for ASDs Minimum total liability sufficient to cause ASD Affected individuals Male-specific risk factors (e.g. fetal testosterone, Y chr) Female-specific protective factors (e.g. estrogens,paternal X chr) (a) (b) FIGURE 1. Multifactorial liability models for autism spectrum disorders (ASDs). (a) Multiple-threshold model in which genetic liability for ASD is normally distributed in the population and the minimum genetic liability sufficient to cause ASD (liability threshold) in females is greater than in males. (b) Multifactorial liability model in which total liability for ASD, including contributions from genetic variation, environment, and other biological factors, is distributed in the population; female-specific factors shift females’ total liability distribution away from, and male-specific factors shift males’ distribution toward, a single threshold. Figure adapted with permission from Reich et al.[31]. Sex differences in autism spectrum disorders Werling and Geschwind 1350-7540 ! 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins www.co-neurology.com3 Fact: Most up-to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism Sex differences Jacquemontet al., (2014) Am J Hum Genetics, doi: 10.1016/j.ajhg.2014.02.001 Suggested possible reasons: • Genetic differences and susceptibility Fact: Most up- to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism Excess of CNVs in females Rare copy number variants (CNVs) De Novo CNVs small medium large small medium large 653 males, 109 females Sex differences https://www.cdc.gov /mmwr /volumes/65/ss/ss6513a1.htm?s_cid=ss6513a1_w Suggested possible reasons: • Genetic differences and susceptibility Fact: Most up- to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism Estimated prevalence of autism among children aged 8 year –USA 2012 Odds ratio = 1.3, p < .01 30% of 2,971 boys 36% of 633 girls Sex differences Lockwood Estrin, (2020) Rev J Autism Dev Disorders, doi: 10.1007/s40489 -020- 00225- 8 Suggested possible reasons: • Genetic differences and susceptibility • Underdiagnosis of autistic women and girls Fact: Most up- to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism Sex differences Suggested possible reasons: • Genetic differences and susceptibility • Underdiagnosis of autistic women and girls • ‘Female autism phenotype’ Fact: Most up- to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism ‘Female autism phenotype’ Lehnhardtet al., (2016) J Autism Dev Disord, doi: 10.1007/s10803 -015- 2558- 7; McFayden et al., (2019) J Autism Dev Dis, doi: 10.1007/s10803 -018- 3838- 9 Psychosocial characteristics of ASD individuals diagnosed late in life (mean ±SD, percentage distribution) Areas of interest percentages by sex in autistic people (age 2 -57; 45% between 2- 6) Sex differences Suggested possible reasons: • Genetic differences and susceptibility • Underdiagnosis of autistic women and girls • ‘Female autism phenotype’ • Camouflaging Fact: Most up- to-date estimate ratio of autistic males to females is 3:1. “Girls who meet criteria for [autism] are at disproportionate risk of not receiving a clinical diagnosis.” Myth: Only boys/men are diagnosed with autism What is camouflaging? Hull et al., (2017) J Autism Dev Disord, doi: 10.1007/s10803 -017- 3166- 5 2524 J Autism Dev Disord (2017) 47:2519–2534 1 3 population; camouﬂaging was simply seen as the way in which everyone tries to ﬁt in or hide less desirable aspects of their personality: Most neurotypicals are camouﬂaging nearly all the time they are in public. (Male, 79) A more pragmatic aspect of this motivation was the desire to obtain jobs and qualiﬁcations, which respond- ents felt were less accessible when they were more visibly ‘autistic’. Many respondents described how they would not have achieved as much had they been more open about their ASC characteristics. Camouﬂaging during these situations was thought to improve employment opportunities, and so enable them to become a ‘functioning member of society’. I’m pretty sure no-one would ever hire me if I didn’t camouﬂage in job interviews. (Other, 27) Camouﬂaging helps to survive in school and college and it is important for keeping jobs. (Female, 27) The desire for assimilation was also prompted by con- cerns for their own safety and wellbeing. Many described being ostracised, verbally or emotionally attacked, and some even reported physical assaults when they had not camouﬂaged their ASC: Fig. 1 Thematic map of the three stages (motivations, camouﬂaging, and consequences) of the camouﬂaging process. Themes are indicated by rectangles; subthemes by ovals Table 2 Number of participants who referenced each theme Theme Number of participants Female (n = 55) Male (n = 30) Other gender (n = 7) Assimilation: “hide in plain sight” 49207 “To know and be known” 42245 Compensation: “to exceed what nature has given” 45227 Masking: “I’m hiding behind what I want people to see” 38187 “I fall to pieces” 44217 “People have a stereotyped view” 3264 “I’m not my true self” 31153 Pressure to ‘fit in’ with neurotypical social communication, individuals with autism may develop coping strategies Camouflaging Autistic Traits Questionnaire –CAT -Q Three subscales: • Masking: Strategies used to hide autistic characteristics or portray a non- autistic persona • Compensation: Strategies used to actively compensate for difficulties in social situations • Assimilation: Strategies that reflect trying to fit in with others in social situations Hull et al., (2019) J Autism Dev Disord, doi: 10.1007/s10803 -018- 3792- 6 Camouflaging and gender Hull et al., (2020) Autism, doi: 10.1177/1362361319864804 Camouflaging and gender Hull et al., (2020) Autism, doi: 10.1177/1362361319864804 Masking: Strategies used to hide autistic characteristics or portray a non- autistic persona Camouflaging and gender Hull et al., (2020) Autism, doi: 10.1177/1362361319864804 Compensation: Strategies used to actively compensate for difficulties in social situations Camouflaging and gender Hull et al., (2020) Autism, doi: 10.1177/1362361319864804 Assimilation: Strategies that reflect trying to fit in with others in social situations Camouflaging, sex, gender and diagnostic timing McQuaid et al., (2022) Autism, doi: 10.1177/13623613211042131 Camouflaging and stigma Perry et al., (2022) J Autism Dev Disord, doi: 10.1007/s10803 -021- 04987- w Higher perceived autism stigma was associated with higher levels of self - reported camouflaging behaviours Autism -related stigma had a negative relationship with mental wellbeing Camouflaging and mental health Bernardin et al., (2021) Autism, doi: 10.1177/1362361321997284 Why are there sex and gender differences in autism prevalence? due to Sex / gender differences in prevalence Overreliance on male research participants Missing sex / gender differences in phenotype Biology Cognition Behaviour Application of male autism phenotype in clinical practice Sex / gender specific autism phenotype? Camouflaging Under -or misdiagnosis Masking Compensation Assimilation Higher levels of co - occurring mental health conditions leads to results in at the levels of due toimpacted by defined by leads to influences UNIVERSAL THEORY OF AUTISM? 5-minute break https://www.qualtrics.manchester.ac. uk/jfe/form/SV_ 73SzZHbfDlnlKh8 Mechanisms and theories Fletcher-Watson & Firth, (2019) Autism Environment Environment A r e l i a b l e m a r k e r s h o u l d s h o w adequate: • Sensitivity: be found in all members of a group • Specificity: be exclusive to all members of that group A u n i v e r s a l t h e o r y t r i e s t o e x p l a i n h o w one modular component (biological, cognitive or a combination of biological and cognitive factors) explains autism in all autistic people Problem: there are many interacting factors What to look for in a good autism theory? 1.Concrete predictions, which generate rigorous tests 2. An interpretation, not just a simple description of the evidence 3. Detailed explanation of the pattern of characteristics in the autism constellation 4. A c a u s a l a c c o u n t 5. Alignment with basic scientific truths, including what we know about typical development 6. Informed by community perspectives and priorities Table 1, Fletcher -Watson & Frith, (2019) Autism Standardisedagainst societal norms The behavioural profile associated with autism characterisedagainst ‘normative standards’ by, e.g., • Use of standardized tests • ‘Typical’ developed is based on narrow sample of e.g., –WEIRD populations – Undergraduate populations – Populations without any mental health difficulties or other conditions • ‘Normative lens’ • Often not informed by community perspectives Fletcher -Watson & Firth, (2019) Autism What are the criteria for a universal autism theory? Universal Theory must be Sensitive / universal Specific All behavioural features of autism Differences in social communication & interactions Specific behaviours or areas of interest Modular component Differences All autistic people Community perspectives Ty p i c a l s t a n d a r d s , e.g., neurotypical developmental trajectories must be to to tries to explain leading to often not informed by caveated by DIFFERENCES IN SOCIAL PROCESSING RECOGNITION OF EMOTION Are there differences in emotion recognition? •Mixed findings as to whether there are differences in emotion recognition between autistic and non -autistic people • Neuroimaging studies have indicated neural differences between autistic and non -autistic people during emotion processing tasks, including differences in activity of the amygdala and posterior fusiform gyrus (fusiform face area) Meyer -Lindenberg et al., (2022) Molecular Autism, doi: 0.1186/s13229- 022-00520- 7 Are there differences in emotion recognition? Methods: - 255- 488 participants –autistic, non -autistic, and/or mild intellectual disability - Participants completed 2 -3 behavioural facial emotion expression tasks and some also completed a fearful face -matching task in the fMRI scanner Meyer -Lindenberg et al., (2022) Molecular Autism, doi: 0.1186/s13229- 022-00520- 7 Research Aim: Explore the role of facial expression recognition as a candidate stratification biomarker in a large group of autistic individuals diverse in age and intellectual ability. Are there differences in emotion recognition? Results (1): - No significant differences between autistic and non -autistic participants in amygdala or fusiform gyrus activation overall or within age groups - Autistic people who performed worse on the emotion recognition tasks, also activated the right amygdala and the fusiform face area less than autistic people who performed well on the emotion recognition tasks Meyer -Lindenberg et al., (2022) Molecular Autism, doi: 0.1186/s13229- 022-00520- 7 Research Aim: Explore the role of facial expression recognition as a candidate stratification biomarker in a large group of autistic individuals diverse in age and intellectual ability. Are there differences in emotion recognition? Results (2): - Autistic people in the lower performing subgroup had more clinical features indicating difficulties with social processing Conclusion: The study identified a subgroup of autistic people (30% of the autistic participants) who may have difficulties identifying facial emotional expressions Meyer -Lindenberg et al., (2022) Molecular Autism, doi: 0.1186/s13229- 022-00520- 7 Research Aim: Explore the role of facial expression recognition as a candidate stratification biomarker in a large group of autistic individuals diverse in age and intellectual ability. Difficulties emotion interpretation or expression Reminder: • Alexithymia: impaired ability to be aware of, explicitly identify, and describe one's feelings Evidence of higher levels of alexithymia in autistic people Meta-analysis of Alexithymia prevalence • Research Question: Aim to explore the prevalence of alexithymia in autistic people • Results: –Prevalence in autistic people: 49.93% – Prevalence in non -autistic people: 4.89% • Conclusion: Autistic people are more likely to experience higher levels of alexithymia compared to non -autistic people Kinnaird et al., (2019) Eur Psychiatry, doi: 10.1016/j.eurpsy.2018.09.004 Does alexithymia impact social features of autism? Participants: - Time point 1 -158 non- autistic and 179 autistic people - Time point 2 –59 non- autistic and 76 autistic people Results 1: Higher alexithymia reported by autistic participants - 47.3% of autistic women and 21.0% of autistic men met the cut- off for clinically relevant alexithymia Research Question: Predict that increasing alexithymia would be associated with elevated social -communication difficulties, anxiety and depression symptoms in autistic adolescents and adults Oakley et al., (2022) Psychological Medicine, doi: 10.1017/S0033291720003244 Does alexithymia impact social features of autism? Results 2: - Difficulties in describingfeeling were associated with self -reported difficulties in social communication - Difficulties in identifyingfeelings were associated with anxiety symptom severity Conclusion: Difficulties in identifying v. describing emotion are associated with differential clinical outcomes in autism Research Question: Predict that increasing alexithymia would be associated with elevated social -communication difficulties, anxiety and depression symptoms in autistic adolescents and adults Oakley et al., (2022) Psychological Medicine, doi: 10.1017/S0033291720003244 Are there differences in emotion recognition? Emotion recognition in others Facial emotional expression research Alexithymia within self B f2B Mixed findings Differences in ability Possibly due to Subgroups of autistic people between Increased clinical social features defined by Higher rates in Autistic people associated with Increased anxiety symptoms DIFFERENCES IN SOCIAL PROCESSING COGNITIVE EMPATHY What is Theory of Mind? Reminder: • Theory of Mind falls under Cognitive Empathy • Cognitive empathy: recognisingand understanding that another person is thinking or feeling something different to what you are thinking or feeling • Theory of Mind: the ability to attribute independent mental states to oneself and others to explain their behaviour Theory of Mind as a ‘primary deficit’ •Older models often called “primary deficit models” • Mindblindness theory of autism Theory of Mind as a ‘primary deficit’ Baron-Cohen et al., (1985) Cognition, doi: 10.1016/0010 -0277(85)90022 -8 Sally -Anne Test Older explanation of the Sally -Anne Test: uiicg‘FF’’’WvMkikUsWyM F’e iyuGxV–pRgWXCsWYZ Neural evidence for mentalisingis inconsistent Participants: 205 autistic and 189 non - autistic participants between 6 -30 years old Hypothesis: Expected autistic individuals to show reduced regional activation in key areas of the social brain in response to the animated shapes Moessnang et al., (2020) Molecular Autism, doi: 10.1186/s13229 -020- 0317- x Research Aim: To d i s c o v e r a n d v a l i d a t e n e u r o f u n c t i o n a l m a r k e r s o f s o c i a l cognition alterations in ASD as a first step for biomarker discovery Neural evidence for mentalisingis inconsistent Results: - Mentalising task led to activation of key regions of the social brain including the posterior superior temporal sulcus and dorsolateral prefrontal cortex - Categorical comparisons between non -autistic and autistic participants did not reveal group differences Moessnang et al., (2020) Molecular Autism, doi: 10.1186/s13229 -020- 0317- x Research Aim: To d i s c o v e r a n d v a l i d a t e n e u r o f u n c t i o n a l m a r k e r s o f s o c i a l cognition alterations in ASD as a first step for biomarker discovery Neural evidence for mentalisingis inconsistent Tw o p o s s i b l e r e a s o n s f o r n o t finding group differences: - Differences in current autistic feature profiles might impact the comparability to older studies - Earlier findings obtained samples that were smaller, more homogeneous, and had potentially different feature profiles Moessnang et al., (2020) Molecular Autism, doi: 10.1186/s13229 -020- 0317- x Research Aim: To d i s c o v e r a n d v a l i d a t e n e u r o f u n c t i o n a l m a r k e r s o f s o c i a l cognition alterations in ASD as a first step for biomarker discovery The Double Empathy Problem Crompton et al.,, (2021) Frontiers for Young Minds, doi: 10.3389/frym.2021.554875 Differences in peer-to-peer transfer of information Method: - ‘Diffusion chain’ technique - 72 adult participants: 3 groups per chain with each 8 people, 24 people in total per chain condition - 30- point story, which followed a bear on a surreal adventure Research Aim: To compare how autistic and non -autistic people interact when in matched (same diagnostic status) or mixed (autistic with non -autistic pairs), in an information -sharing context Crompton et al., (2020) Autism, doi: 10.1177/1362361320919286 Differences in peer-to-peer transfer of information Results (1): - Autistic people share information with other autistic people as well as non- autistic people do with other non- autistic people - However, when there are mixed groups of autistic and non -autistic people, much less information is shared Research Aim: To compare how autistic and non -autistic people interact when in matched (same diagnostic status) or mixed (autistic with non -autistic pairs), in an information -sharing context Crompton et al., (2020) Autism, doi: 10.1177/1362361320919286 Differences in peer-to-peer transfer of information Results (2): - Participants were also asked how they felt they had got on with the other person in the interaction - The people in the mixed groups experienced lower rapport with the person they were sharing the story with Research Aim: To compare how autistic and non -autistic people interact when in matched (same diagnostic status) or mixed (autistic with non -autistic pairs), in an information -sharing context Crompton et al., (2020) Autism, doi: 10.1177/1362361320919286 Differences in peer-to-peer transfer of information Conclusions: - Difficulties in autistic communication are apparent only when interacting with non -autistic people and are alleviated when interacting with autistic people. - Autistic and non -autistic people do not significantly differ in how accurately they recall information from peers of the same neurotype, butselective difficulties occur when autistic and non -autistic people are sharing information - This occurs alongside significantly lower rapport within mixed groups Research Aim: To compare how autistic and non -autistic people interact when in matched (same diagnostic status) or mixed (autistic with non -autistic pairs), in an information -sharing context Crompton et al., (2020) Autism, doi: 10.1177/1362361320919286 Is the “Mindblindness ” theory out of date? Mentalising differences neural level evidence is aQ?Ueoinc Inconsistent due to Differences in autistic people’s feature profiles Prior research having small homogenous groups Medical model explored via Social model The Double Empathy Problem Breakdown in mutual understanding as a Autistic and non - autistic people between Lower rapport possibly contributes to Thank you for listening –Any Questions? Support information I hope that you enjoyed the content of the lecture, however, if any of the materials discussed during this lecture was difficult for you to attend to or process due to mental health difficulties, please do not hesitate to visit any of the following websites for support information, contact your Academic Advisor or contact your GP: - Links to list of support services: https://www.studentsupport.manchester.ac.uk 24- hour support is offered by: - Qwell : https://www.studentsupport.manchester.ac.uk/taking -care/qwell/ - Health Assured: https://www.studentsupport.manchester.ac.uk/taking - care/mental-health- helpline/

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