Social Media Use Motives and Mental Health Among Adolescents: Cross-Sectional Study

Background: Social media use is ubiquitous among young people, yet its relationship with mental health may depend on why it is used. Understanding the motives underlying social media engagement may provide greater insights into its potential risks and benefits than considering overall use alone. Objective: This study aimed to identify latent dimensions of motives for social media use and examine their associations with anxiety, depression, well-being, and resilience in a sample of older adolescents. Methods: Cross-sectional data were drawn from the BrainWaves study (N=6820; mean age 16.72, SD 0.74 years). Social media use motives were assessed using 10 items and exploratory factor analysis identified 2 latent dimensions: avoidant-escapist use (boredom, addiction, distraction, and procrastination) and social-recreational use (socializing, communication, entertainment, and observation). Confirmatory factor analysis was used to estimate factor scores, which were examined using linear regression. Structural equation modeling estimated associations with anxiety, depression, well-being, and resilience, with secondary analyses accounting for gender and deprivation. Results: Linear regression analyses showed that avoidant-escapist motives were associated with higher anxiety and depressive symptoms, whereas social-recreational motives were associated with lower symptoms. No interactions with gender or deprivation were observed. The structural equation model showed acceptable model fit (comparative fit index=0.945; root mean squared error of approximation=0.057; standardized root mean squared residual=0.054). Avoidant-escapist motives were associated with higher anxiety (β=0.543) and depressive symptoms (β=0.629) and lower well-being (β=−0.610) and resilience (β=−0.469). Conversely, social-recreational motives were associated with lower anxiety (β=−0.228) and depressive symptoms (β=−0.342) and higher well-being (β=0.362) and resilience (β=0.242; <.001 in all cases). These patterns remained consistent after accounting for gender and deprivation. Conclusions: Social media use shows different associations with older adolescent mental health depending on underlying motives. Focusing on why young people engage with social media rather than on overall use alone may provide a more informative framework for understanding potential risks and benefits. Given the cross-sectional design, causal inferences cannot be made.

Psychosocial distress is a stronger correlate of psychiatric symptom burden than cannabis dependence in young adults: a functional connectome and structural equation modeling study

IntroductionCannabis is widely used among young adults and often viewed as a primary driver of adverse psychiatric and neural outcomes, yet the psychosocial and socioeconomic context in which use occurs is rarely modeled jointly with neural and behavioral measures.MethodsUsing the Human Connectome Project young-adult sample (analytic N = 1,010–1,016), we employed structural equation modeling to estimate the relative contributions of cannabis, latent psychosocial distress, socioeconomic status, and familial psychiatric risk to three indices of psychiatric symptom burden, while adjusting for polysubstance, alcohol, and nicotine exposure and for family clustering. Cannabis exposure was operationalized in parallel as DSM-IV dependence and as heavy use. Resting-state functional connectomes were examined using graph metrics and network-based statistics, and finite mixture models were used to test for discrete vulnerable subgroups.ResultsPsychosocial distress was the dominant predictor of psychiatric symptom burden across all three outcomes (standardized β = 0.41–0.70), substantially exceeding that of cannabis (total β = 0.05–0.19), which reached significance for only one outcome under the dependence definition. Larger unadjusted group differences (Cohen’s d = 0.28–0.75) attenuated sharply once distress and demographic factors were modeled. Lower socioeconomic status and greater familial psychiatric risk were each associated with elevated distress, whereas cannabis was not (all indirect β ≤ 0.01, all confidence intervals spanning zero). Neither graph metrics nor the network-based statistic revealed an association between cannabis dependence and the functional connectome, despite approximately 80% power to detect d≈ 0.30, and the fitted finite mixture models did not identify a robust and interpretable cannabis-vulnerable subgroup.DiscussionIn a large young-adult sample, psychosocial context rather than cannabis exposure accounted for most of the explained variance in psychiatric symptom burden, and no cannabis-related signature was detectable in the functional connectome. The cross-sectional design cannot establish causal ordering, and because the distress and symptom measures share self-report method variance that the coarse binary exposure measures do not, the relative magnitudes should not be interpreted as a comparison of aetiological importance. These findings nonetheless suggest that risk models and neuroimaging analyses that omit psychosocial context may attribute to cannabis variance shared with distress, disadvantage, and familial risk.

Nogo-related frontal theta-band activity after magnetic seizure therapy in treatment-resistant depression: an exploratory task-based EEG study

IntroductionMagnetic seizure therapy (MST) is an emerging convulsive treatment for treatment-resistant depression (TRD) with efficacy comparable to electroconvulsive therapy and superior cognitive safety. However, its neurophysiological mechanisms—particularly those underlying conflict monitoring and response inhibition—remain unclear. Frontal midline theta-band activity indexes anterior cingulate–prefrontal cognitive-control activity and may reflect inefficient neural processing in depression.MethodsIn this open-label, single-arm trial, 19 patients with TRD (16 female; mean age 20.89 ± 8.16 years; 10 of the 19 participants were minors) received 6–12 MST sessions using 100-Hz double-cone coil stimulation targeting bilateral F3/F4 frontal regions. Clinical assessments, including the 24-item Hamilton Depression Rating Scale (HAMD-24), Hamilton Anxiety Rating Scale (HAMA), Montgomery-Åsberg Depression Rating Scale (MADRS), and 64-channel Go/Nogo task-based electroencephalography (EEG), were performed before and after treatment. EEG outcome measures included event-related potentials (N2/P3), time-frequency power (theta/beta), microstate dynamics, and theta–beta cross-frequency coupling.ResultsFollowing MST, patients showed significant reductions in depressive symptoms (HAMD: 40.53 ± 10.25 to 22.53 ± 11.21; MADRS: 35.68 ± 8.84 to 20.68 ± 11.12). Response rates ranged from 42.1% to 57.9%. Behavioral data could not be formally analyzed due to incomplete data storage. At an uncorrected threshold, total Nogo-related theta-band activity (4–7 Hz) at Fz and FCz was lower after treatment; however, these effects did not survive FDR correction. ERP amplitudes did not change significantly. Exploratory microstate analysis showed nominal changes in Map4 AUC during Go processing and Map2 duration during Nogo processing; none survived FDR correction. These parameters should not be interpreted as reflecting changes in neural activation or inhibitory preparation. No significant changes were observed in theta–beta cross-frequency coupling or Go-condition theta power.DiscussionThese preliminary findings suggest that MST was associated with short-term symptom improvement and lower total Nogo-related theta-band activity at an uncorrected threshold. Because no control group, complete behavioral data, normative comparison, Time × Condition interaction, or EEG-clinical association was available, these findings cannot establish causal mechanisms, condition specificity, improved neural efficiency, or biomarker utility. Replication in adequately powered controlled studies with complete behavioral and longitudinal clinical measures is required.

Impulsivity is the principal correlate of problematic internet use in young adults with attention-deficit/hyperactivity disorder: a cross-sectional clinical study

IntroductionAttention-deficit/hyperactivity disorder (ADHD) and problematic internet use share clinical features such as impulsivity, emotional dysregulation and impaired inhibitory control, but most evidence comes from children, adolescents and non-clinical samples. It remains unclear which dimension carries this association in adults with a formally established diagnosis.MethodsRetrospective, cross-sectional study of young adults (aged 18 to 38 years) consecutively assessed at a second-level outpatient service for adult ADHD in Milan, Italy. Diagnosis was established with a semi-structured diagnostic interview (DIVA-5), which also yielded symptom counts for childhood and adult inattention and hyperactivity/impulsivity. Participants completed the Internet Addiction Test, the Barratt Impulsiveness Scale, the Difficulties in Emotion Regulation Scale, measures of depressive and anxiety symptoms, a quality-of-life questionnaire and two ADHD symptom questionnaires. Impulsivity and emotional dysregulation were prespecified variables of interest. Analyses comprised correlations with correction for multiple comparisons, group comparisons, multivariable linear regression and bootstrapped mediation.ResultsTwo hundred and fourteen patients had a valid Internet Addiction Test score (mean 52.96, standard deviation 15.16); 22.9% were average users, 62.1% problematic users and 15.0% highly problematic users. Problematic internet use correlated with impulsivity (rho = 0.345) and emotional dysregulation (rho = 0.229), both surviving corrections, while no significant association was detected with the four interview-derived symptom counts (rho from 0.004 to 0.119), the two symptom questionnaires, quality of life or trait anxiety. In the multivariable model impulsivity was the strongest correlate (b = 0.317, p < 0.001; standardized beta 0.33), whereas emotional dysregulation was weaker and varied across specifications (b = 0.091, p = 0.052). Severity, presentation, symptom counts and demographic and clinical covariates were not significantly associated with the outcome. Mediation analysis identified an indirect association between severity and internet use through impulsivity (indirect effect 1.56, 95% confidence interval 0.41 to 2.96).DiscussionIn young adults with ADHD, impulsivity is the dimension most consistently related to problematic internet use, whereas no association was detected with core symptom severity; the confidence intervals remain compatible with weak associations. Assessment of online behavior should not be restricted to the most severely affected patients. The design does not support causal inference.

Dance- and movement-based interventions for autism spectrum disorder: a systematic review and exploratory meta-analysis

ObjectivesAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by difficulties in social communication, restricted and repetitive behaviors, and impaired adaptive functioning. Although behavioral and developmental interventions remain central to ASD management, the effects of dance- and movement-based interventions (DMIs) across different functional domains have not been fully clarified. This study aimed to systematically evaluate the effects of DMIs on overall autism symptom severity, social communication/social responsiveness, and motor function in individuals with ASD.MethodsThis systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible DMIs included formal dance/movement therapy or dance movement psychotherapy and other structured dance- or purposeful movement programs in which movement constituted a central therapeutic or rehabilitative component. PubMed, Web of Science, Embase, the Cochrane Library, and PsycINFO were searched from inception to August 2026. Randomized and non-randomized controlled studies were not combined in the same primary meta-analysis. Randomized studies reporting sufficiently comparable outcomes were synthesized using standardized mean differences (SMDs) with 95% confidence intervals (CIs) under random-effects models. Risk of bias was assessed using RoB 2 and ROBINS-I, and certainty of evidence was evaluated using GRADE.ResultsTwelve controlled studies involving individuals with ASD were included and showed substantial variation in intervention format, comparator conditions, outcome measures, and risk of bias. Exploratory RCT-only meta-analyses included three studies for social communication/social responsiveness (n = 76; SMD = −0.68, 95% CI: −1.58 to 0.23; p = 0.15; I² = 72%) and three studies for motor function (n = 55; SMD = 2.04, 95% CI: −0.52 to 4.61; p = 0.12; I² = 92%); certainty of evidence was very low for both domains. Overall autism symptom severity was represented by one randomized trial (n = 30; SMD = −3.09, 95% CI: −4.19 to −1.99), resulting in low-certainty evidence and precluding pooling. Narrative synthesis identified potentially favorable signals in joint engagement, cooperative behavior, interpersonal synchrony, emotion inference, and gesture imitation, but these findings arose from small and heterogeneous studies.ConclusionThe current evidence base for DMIs is small, heterogeneous, and of low to very low certainty. Structured narrative synthesis suggests possible domain-specific benefits, whereas exploratory pooled estimates for social communication/social responsiveness and motor function remain inconclusive. DMIs may have potential as complementary interventions for ASD, but larger, methodologically rigorous randomized trials with standardized interventions and outcome measures are required.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420261495230, identifier CRD420261495230.

Latent profiles of day-7 cognitive performance and associated ecological factors in older adults after elective surgery: an observational study

ObjectiveTo identify latent profiles of postoperative day-7 cognitive performance among older adults after elective surgery and to explore demographic, behavioral, clinical, and social factors associated with profile membership.MethodsThis prospective observational study included 308 consecutively recruited patients aged ≥65 years who underwent elective surgery under general anesthesia. Cognition was assessed with the Mini-Mental State Examination (MMSE), and latent profile analysis used five study-defined postoperative domain scores. Candidate correlates were organized with the health ecological model and examined using univariate tests and posterior-probability-weighted exploratory multinomial regression.ResultsThree severity-ordered profiles were identified: low (n=83, 26.9%), intermediate (n=72, 23.4%), and high postoperative cognitive performance (n=153, 49.7%). Older age and lower education were associated with both lower-performance profiles. Greater activities-of-daily-living dependence, lower income, and living alone were associated with the low-performance profile, whereas poorer sleep was associated with the intermediate-performance profile.ConclusionsDay-7 MMSE performance formed a severity gradient across three data-driven profiles. These cross-sectional profiles do not demonstrate postoperative decline, a clinical diagnosis, or an effect attributable specifically to general anesthesia. Longitudinal studies with retained baseline scores, appropriate comparison groups, and broader neuropsychological assessment are required before profile-specific interventions can be recommended.

Trajectories of perinatal depression, anxiety, perceived stress, sleep quality, and psychological flexibility from pregnancy to six months postpartum and their relationship with the number of abortions and deliveries

BackgroundPerinatal psychological distress is common and longitudinally heterogeneous, yet little is known about how obstetric history shapes symptom trajectories. We identified trajectories of depressive symptoms, perceived stress, sleep quality, psychological flexibility, and anxiety from early pregnancy to 24 weeks postpartum and examined their associations with the number of previous abortions and deliveries.MethodsIn this prospective longitudinal cohort study, 254 pregnant women were recruited at 13 weeks of gestation from a tertiary hospital in China and assessed eight times through 24 weeks postpartum using the EPDS, CPSS, PSQI, CompACT, and GAD-7. Group-based trajectory modeling identified latent trajectory classes. Logistic regression examined associations with obstetric history, adjusting for sociodemographic covariates.ResultsThree trajectories emerged for depressive symptoms (low-stable, 38.98%; mild-stable, 47.24%; moderate-increasing, 13.78%), perceived stress (low-stable, 31.89%; moderate-increasing, 16.54%; high-stable, 51.57%), and sleep quality (good-stable, 50.00%; mild-worsening, 38.58%; persistent poor, 11.42%); two emerged for psychological flexibility (continuously declining vs high-stable) and anxiety (low-stable, 93.70%; moderate, 6.30%). Compared with women without abortion history, those with two or more abortions had higher odds of mild-stable (OR = 5.14) and moderate-increasing depression (OR = 19.12), mild-worsening (OR = 3.06) and persistent poor sleep (OR = 5.51), and declining psychological flexibility (OR = 2.61); one abortion was associated with increased stress (OR = 2.54 and 2.16) and moderate anxiety (OR = 4.18). Number of deliveries was not associated with any trajectory.ConclusionsPrevious abortions predict adverse perinatal psychological trajectories in a dose-dependent manner. Routine assessment of abortion history at the first prenatal visit could help target early screening and tailored interventions.

Symptom severity at first digital self-assessment is not associated with greater subsequent booking of professional care on a Saudi mental health platform: a cohort study of 5,524 users

BackgroundDigital mental health platforms routinely deliver self-administered symptom scales on the assumption that users with more severe symptoms are more likely to enter professional care. That assumption is rarely tested with linked participant-level data, and almost never in the Arabic-speaking Gulf.ObjectiveTo examine whether symptom severity at a user’s first self-assessment predicts booking a professional session within 90 days, across four symptom domains, on a Saudi commercial digital mental health platform.MethodsRetrospective cohort analysis of 10,100 first assessments — depression, generalized anxiety, panic, and obsessive-compulsive scales — contributed by 5,524 unique users of the Talbinah platform and linked to demographic and session-booking records through a pseudonymous (SHA-256 hashed) user identifier. The primary outcome was any session booked within 90 days after the assessment. Severity was banded by the established cut-offs for the PHQ-9 and GAD-7, and by percentage of scale maximum for the panic and obsessive-compulsive measures, for which no severity thresholds are established. Per-scale multivariable logistic regression estimated adjusted odds ratios (aOR) for severity band (reference None/Minimal), adjusting for age, sex, and device type. Pre-specified sensitivity analyses used 30- and 180-day windows, an attended-session outcome, percentage-of-maximum banding applied to all four scales, and a subset with no prior booking (incident help-seeking). Reporting followed STROBE.ResultsUsers were young (mean age 26.7 years, SD 8.1) and predominantly female (78.1%); 16.6% of first assessments were followed by a booking within 90 days. No severity band on any of the four scales was associated with higher odds of booking (severe vs None/Minimal: depression aOR 0.71, 95% CI 0.51–1.01; generalized anxiety 0.76, 0.49–1.16; panic 0.83, 0.52–1.31; obsessive-compulsive 1.01, 0.59–1.74). One severity contrast was statistically significant, in the opposite direction: moderate depression was associated with lower odds of booking (0.66, 95% CI 0.45–0.97). Older age (aOR ~1.2 per decade) and iOS device use (1.4–2.0) were associated with booking across scales. This absence of a positive severity–booking association was consistent across 30-, 90-, and 180-day windows, the attended-session outcome, and percentage-of-maximum banding; a nominal association for moderate anxiety seen under percentage banding did not persist with validated GAD-7 cut-offs. Estimates for the panic and obsessive-compulsive scales, which lack established severity thresholds, are exploratory.ConclusionsOn a Saudi digital mental health platform, greater self-reported symptom severity at first assessment was not followed by higher subsequent booking of professional care, consistently across specifications, and for moderate depression was associated with lower odds of booking; demographic and access-related attributes showed stronger associations with booking than symptom load did. Self-screening on its own appears insufficient as a pathway into care.

Understanding the Different Types of Psychological Evaluations

If your child is struggling with mental health, behavior, or learning challenges, the first step to getting them the support they need is to seek a professional assessment. Whether you’ve been motivated to do so by the child’s teacher, the school, or your own concerns, there are several types of evalutations to choose from. And it can be hard to tell which one would be the best choice for your child.

There are three main types of evaluations: diagnostic, psychoeducational, and neurospychological.

A diagnostic evaluation is typically all you need for your child to receive a diagnosis of a mental health disorder and start a treatment plan.

A psychoeducational assessment is an evalution typically performed through a school’s Committee on Special Education (CSE) to see if a child qualifies for accommodations. This usually includes cognitive and academic testing, as well as social-emotional rating scales.

A neuropsychological evaluation is the most complete assessment of a child’s overall social-emotional, cognitive, academic, and neuropsychological functioning (including language, visual-motor, attention, executive functioning, and learning and memory skills). It’s especially useful for children with complex symptoms or learning challenges.

If you are looking to determine whether your child has a learning disorder, they’ll typically need either a full neuropsychological evaluation or one targeted for a specific learning disorder.

Preparation

For any of the three types of evaluations, it’s suggested that you do the following in advance:

  • Gather documents: Before the first visit, you will need to collect any relevant health and behavior records for your child, which could include medical records, past evaluations, school reports, and developmental screenings conducted by the child’s pediatrician.
  • Record observations: Take note of your child’s behaviors, especially when they seem atypical or out of character. Keep track of when they occur, as well as how frequently. This will be helpful for the evaluator to know.
  • Bring snacks and water: Since these examinations take several hours, you and your child will most likely need to take breaks.

Diagnostic evaluation

What it is: A diagnostic evaluation is a focused assessment to determine whether your child meets the criteria for a specific mental health or neurodevelopmental disorder — including depression, anxiety, ADHD, or autism. For example, if your child struggles with intrusive thoughts that make them anxious and they develop rituals to keep their anxiety at bay, a mental health professional will most likely add specific testing for OCD to their evaluation.

Purpose: “Diagnostic evaluations are primarily the gateway for receiving treatment,” explains Adam Zamora, PhD, a senior neuropsychologist and director of the Gund Learning and Diagnostic Center at the Child Mind Institute. “We want to know what diagnostic criteria they meet to figure out which treatment would be best for that diagnosis.”

Qualified provider: Typically, the evaluation is conducted by a clinical psychologist, psychiatrist, or another qualified mental health professional.

Time commitment: The evaluation process is about 4 hours total. This includes an interview with parents that will take 1–2 hours, a one-hour session with the child, and a one-hour feedback session with the family.

Evaluation process:

  • The clinician will collect information about your child’s developmental history as well as your family history. So they’ll usually speak to any previous treatment provider your child has had as well as their teacher, says Dr. Zamora. Then you’ll be interviewed, which can be done virtually.
  • You will fill out questionnaires about your child’s daily habits and behaviors. And you’ll fill out a rating scale that will help to quantify your child’s symptoms and compare them to the norm. This can be helpful for balancing out the information received during the interview, says Dr. Zamora. “I’ve had some parents tell me that their kid has all these significant challenges. And then they fill out the rating scales and everything is within age expectation. Or it can be the other way around.”
  • Then the child comes in for observation and evaluation. A parent could be present, depending on the situation, notes Lara Litvinov, PhD, a senior psychologist and director of the ADHD and Behavior Disorders Center at the Child Mind Institute. During this observation, the clinician will administer standardized assessments, which typically are tailored to their symptoms. For instance, if it’s thought that a child may have autism, they will be tested on their social communication skills and whether they show repetitive behaviors. The evaluator might observe if the child has difficulty maintaining eye contact as they talk, or see if they play with toys in the exact same way over and over again. If it’s thought that a child may have ADHD, the clinician will test their cognitive and executive functioning abilities. They might have the child solve puzzles or play memory games.
  • Once the clinician has been able to review the information collected from the evaluation, there will be a feedback interview. Along with a diagnosis, the clinician will provide their recommendations for treatment. They will also suggest further testing if necessary.

Report: Shortly after the feedback interview, you will receive a formal written report of the evaluation results. This will consist of brief background information, a diagnosis, and recommendations.

Cost: Depending on the location, clinician experience, the specificity of testing, and insurance coverage, these evaluations can range from $300 to $4,000. Community health centers may offer lower rates, while a private practice with licensed psychologists or psychiatrists will charge more.

Drawbacks: While a diagnostic evaluation is good for starting treatment, it doesn’t provide an in-depth look at all aspects of your child’s brain functioning. If a child’s symptoms are complex, they’re thought to have a learning disorder, or they’re struggling with multiple mental health disorders, it may be more helpful to have a neuropsychological evaluation (see below). Typically, the evaluator will let you know if they think your child needs further assessment.

Psychoeducational evaluation

What it is: A psychoeducational evaluation determines if a student is eligible for special education or accomodations at school. It’s an assessment of their cognitive, academic, social, and emotional functioning compared to other children the same age. It also lets the school know the type of services the child will need.

Purpose: Children with mental health and learning disorders often need accommodations and services at school to have the same opportunities as children who don’t have these challenges. They could get this through an Individual Education Program (IEP) or a Section 504 plan. Since a school needs to determine that a student’s academic abilities are significantly impaired, an existing diagnosis may not be enough. Many schools require a psychoeducational assessment within a certain window of time after receiving a referral from a medical provider or a formal written request from you.

Qualified provider: This evaluation is conducted by a registered school psychologist.

Time commitment: The evaluation can take 4–6 hours in total. You and your child may be asked to come in for a whole school day or for separate sessions over the span of a few weeks. The testing can be stressful for the child, Dr. Litvinov explains, so it can be helpful to break it up into several sessions.

Evaluation process:

  • In addition to the health records you provide, the psychologist will have asked your child’s teachers to complete a questionnaire about their learning and behavior.
  • The psychologist will interview you about your child’s academic and developmental background, family history, and any concerns you have. You will likely be asked questions about how your child handles school assignments, what their academic strengths and weaknesses are, as well as notes you’ve received from their teachers. If your child is under 10, the examiner likely will ask you to fill out questionnaires about your child’s social-emotional and behavioral functioning.
  • The psychologist will then evaluate your child for several hours. You won’t be permitted in the room with them, but you can wait outside.

What’s tested:

Cognitive functioning: These assessments often include verbal or visual tests to examine verbal and nonverbal reasoning, certain types of memory, and the speed at which your child processes and responds to information. They will also be asked to engage in problem-solving tasks.

Social and emotional functioning: The child’s parents and teachers will complete questionnaires to gauge their social and emotional functioning. Depending upon the child’s age, they may fill out their own questionnaires as well.

You will then have a feedback session with the psychologist. Depending on their age, your child can be included in this session. During this time, you will go over the results, which can identify your child’s learning strengths and challenges and recommended supports.

In some cases, the evaluation may also result in a formal diagnosis of a specific learning or language disorder, depending on the professional conducting the evaluation and state licensing requirements.

Report: Both you and the school will receive a written report that includes detailed results, and provide suggestions for accommodations and interventions needed. It will also say if your child is eligible for separate special education classes.

Cost: The evaluation is free if it is conducted by the school. You will only need to pay out of pocket if you seek a private assessment. This can cost between $1,000 and $6,000. Your insurance may cover some of it, depending on your provider.

Drawbacks:

Given its purpose, this evaluation focuses only on academic skills, learning, and how your child performs in school. In many states, including New York, a school psychologist can’t give a formal diagnosis, Dr. Zamora adds. A psychoed assessment may not be comprehensive enough to highlight all key challenges your child may be experiencing.

Additionally, there could be delays in getting an assessment due to a backlog in requests or a shortage in school psychologists. School officials could also refuse to provide one altogether if they determine your child is not qualified based on specified criteria.

Neuropsychological evaluation

What it is: A neuropsychological evaluation is the examination of a child’s overall social-emotional, cognitive, academic, and neuropsychological functioning — and how they affect each other. This is often recommended for children who have complex issues, where a diagnostic evaluation might be too narrow. It can be helpful for those with multiple mental health disorders, neurodevelopmental disorders such as autism and ADHD, learning disorders, or traumatic brain injury.

A neuropsychological exam takes several sessions, so it can provide a more complete, comprehensive picture of what might be going on. “We’re not just seeing one snapshot of a kid, but multiple snapshots,” Dr. Zamora explains. “And we’re sometimes looking to see if there is a very significant difference in how they’re performing on a Wednesday morning versus a Thursday afternoon, for example. So if you have a kid with ADHD or executive functioning challenges, they may seem okay one day. Then you test them on another day and they’re more inattentive or dysregulated, so you’re going to get a different presentation.”

Purpose: There can be several reasons for seeking this type of testing:

  • Learning disability or school accomodations: Both neuropsychological and psychoeducational assessments can be used to get a child accomodations at school, Dr. Litvinov says. But a neuropsychological evaluation can reveal underlying or more nuanced weaknesses that a psychoed evaluation missed, determining the child’s need for accomodations. So if you are unsatisfied with the assessment your child received or if you are unable to get one from the school, a neuropsych evaluation can be useful.
  • Complex symptoms: Sometimes a child displays challenges and behaviors that can relate to more than one mental health disorder. For instance, ADHD and anxiety can look similar in some children, so testing that covers a wider range of functioning may be needed to get to the bottom of the issues your child is dealing with — and identify the type of support they need. Trauma-related symptoms can also be difficult to pinpoint. “A lot of kids that I see have trauma. And there’s a lot of overlap of trauma symptoms and anxiety symptoms,” Dr. Litvinov explains. “You don’t want to be quick to judgment.”
  • Ineffective treatment: In some cases, a child may have received a diagnosis and has been getting treatment for a while, but it isn’t working. “We want to see if there’s something that we’re missing,” Dr. Litvinov explains. “And with older kids, they might be reporting less, so parents might know less.”
  • Flexibility: Sometimes, given cost and scheduling contraints, a family can request a more focused neuropsychological evaluation. “This looks like what I would call a beefier psychoed evaluation,” Dr. Zamora explains. “So if somebody came to us and was considering just testing for a learning disorder, I’d take a deeper dive into those academic areas to get a sense of which disorder it is. Is this actually dyslexia? Is it dysgraphia?” This evaluation usually takes 3–4 hours.

Qualified provider: This evaluation is typically conducted by a team of clinical psychologists, including a trained neuropsychologist.

Time commitment: The evaluation takes about 6–9 hours of testing in total, with 2–3 additional hours dedicated to interviews and a feedback session between you and the neuropsychologist. You and your child could be asked to come in for the whole day or separate sessions over the course of several days a week.

Evaluation process:

  • The clinican will look at medical records, school reports, and any previous evaluations. They may also speak directly to your child’s teacher and ask them to fill out questionnaires regarding your child’s learning abilities and behavior.
  • You will be interviewed about your child’s background, your family history, and concerns you have.
  • Your child will then spend one-on-one time with the neuropsychologist, in which they will be interviewed and given a series of assessments that examine the following:  
    • Cognitive and intellectual abilities
    • Short- and long-term memory and learning skills
    • Executive functioning
    • Speed and efficiency of processing
    • Concentration and attention
    • Language functioning 
    • Gross and fine motor skills
    • Visual-spatial skills
    • Emotional functioning
    • Social skills and social language
    • Academic functioning, including reading, writing, and math skills

Report: Afterward, you will receive a detailed written report that includes test results, interpretations, diagnoses, and recommendations. It can be used to support a request for a 504 plan or an IEP from your child’s school — and provide guidance to school staff on determining the right type of services for your child. The clinician may want to have a follow-up appointment to discuss the report and answer any questions you may have, but that is not always required.

Cost: Due to the time and depth of the evaluation, the cost of this testing can range from $1,500 to $10,000. While most evaluators may be out-of-network providers, there is a chance that a portion of this kind of evaluation may be covered by insurance.

Drawbacks: It can take a long time to schedule this test, as there is often a waiting list for child and adolescent neuropsychologists. This is also the most expensive examination out of the three.

Why knowing the difference is important

Dr. Litvinov emphasizes that it’s imperative that you’re aware of what each evaluation type entails. “I talk to parents whose child has received a diagnosic evaluation, and they’re frustrated or confused because they expected a detailed report that you typically get from a neuropsych evaluation,” she explains.

Some families wait months for a neuropsychological evaluation, not realizing that they can get treatment started for their child with a diagnostic evaluation or that a school can provide a free psychoeducational evaluation.

Understanding each type of evaluation will help you get your child the support they need.

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