Neural basis of working memory in ADHD: Load versus complexity

Mukherjee, Prerona; Hartanto, T. A.; Iosif, Ana‐Maria; Dixon, J. Faye; Hinshaw, Stephen P.; Pakyürek, Murat; Bos, Wouter van den; Guyer, Amanda E.; McClure, Samuel M.; Schweitzer, Julie B.; Fassbender, Catherine

doi:10.1016/j.nicl.2021.102662

Cited by 14 publications

(10 citation statements)

References 78 publications

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“…Most studies using fMRI or fNIRS employed some variant of an n ‐back working memory task. For instance, several studies implemented a letter n ‐back paradigm (Burgess et al, 2010; Ehlis et al, 2008; Mukherjee et al, 2021; Schecklmann et al, 2013; Sheridan et al, 2007; Stevens et al, 2016; Valera et al, 2010). Others used a visuospatial n ‐back task with shapes as stimuli (Ko et al, 2013; van Ewijk et al, 2015; Vogan et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…Others used a visuospatial n ‐back task with shapes as stimuli (Ko et al, 2013; van Ewijk et al, 2015; Vogan et al, 2014). Memory load also varied, with some studies contrasting a 1‐back versus 2‐back condition (Ko et al, 2013; Vogan et al, 2014), whereas others used a 0‐back versus 2‐back (Burgess et al, 2010; Mukherjee et al, 2021; Schecklmann et al, 2013), or even a 4‐level parametric block design with 0‐back, 1‐back, 2‐back and 3‐back blocks (Mattfeld et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…Studies using fMRI or fNIRS have successfully identified differential activation patterns across the spectrum of conditions, revealing both general and condition‐specific insights. In ADHD, for instance, BOLD‐based measures have revealed a general trend of reduced activation under increased cognitive load (Burgess et al, 2010; Ehlis et al, 2008; Ko et al, 2013; Mukherjee et al, 2021; Schecklmann et al, 2013; Stevens et al, 2016), yet uncovering a subset of individuals with unimpaired working memory who exhibit similar activation patterns to controls (Mattfeld et al, 2016). This heterogeneity extends to gender‐specific effects, with male ADHD participants displaying lower WM‐related activity than their female counterparts (Valera et al, 2010).…”

Section: Resultsmentioning

confidence: 99%

“…In ADHD, several studies found an interaction effect between diagnosis and cognitive load on brain activation, such that both ADHD and control groups showed increased activation of WM-related brain regions such as the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex and striatum under high cognitive load, but the increase in activation was significantly lower in the ADHD group compared with controls, pointing to a brain activation deficit in ADHD during increased-load working memory tasks (Burgess et al, 2010;Ehlis et al, 2008;Ko et al, 2013;Mukherjee et al, 2021;Schecklmann et al, 2013;Stevens et al, 2016). Only one of these studies included an intervention and observed an increase in WM-related ADHD brain activity in several frontal, parietal and temporal lobe regions following working memory training (Stevens et al, 2016).…”

Section: Bold-based Measures Of Cognitive Loadmentioning

confidence: 99%

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Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

Le Cunff,

Dommett,

Giampietro

2023

Eur J of Neuroscience

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Working memory is integral to a range of critical cognitive functions such as reasoning and decision‐making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near‐infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye‐tracking. Notably, eye‐related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level‐dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye‐related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well‐being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Bold-based Measures Of Cognitive Loadmentioning

confidence: 99%

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Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

Le Cunff,

Dommett,

Giampietro

2023

Eur J of Neuroscience

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“…WM is a short-term memory that allows the acquisition, storage, and manipulation of new information on a temporary basis (Baddeley, 2012). It has been the subject of investigations in the adult population, with or without an associated clinical condition (Martinussen et al, 2005) and has been identified as one of the most impaired EFs in individuals with ADHD (Diamond, 2013;Areces et al, 2018;Mukherjee et al, 2021), which may negatively influence several areas of life, such as academic achievement, emotional processing and social relationships (Alloway, 2006;Mukherjee et al, 2021). Several authors have argued that WM deficits are more frequent in the predominantly inattentive and the combined subtypes, since there seems to be a relationship between attention deficits and WM functioning (Baddeley and Hitch, 1974;Milla-Cano and Gatica-Ferrero, 2020;Zhao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The effect of a virtual reality based intervention on processing speed and working memory in individuals with ADHD—A pilot-study

Cunha

Campos

Simões-Silva

et al. 2023

Front. Virtual Real.

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Introduction: This study aimed to evaluate the effectiveness of a virtual reality based intervention in processing speed and working memory in students with ADHD symptomatology.Methods: A randomized experimental study was conducted, with a sample consisting of 25 adult participants recruited from the Escola Superior de Saúde do Politécnico do Porto. The participants were allocated into two groups: a passive control group and an intervention group that completed 10 sessions using virtual reality-based games from the Enhance VR app. The intervention included 6 games: Whack-a-mole, Shuffled, Assembly, React, Memory Wall, and Maestro. The participants underwent pre- and post-intervention evaluations using the Southwestern Assessment of Processing Speed (SWAPS) and the Sequence of Letters and Numbers and Spatial Location of the Wechsler Adult Intelligence Scale - 3rd Edition - WAIS-III. Descriptive statistics were used to characterize the sample and a mixed ANOVA was used to test the effectiveness of the intervention.Results: There was an improvement in the results of processing speed in the group exposed to the intervention (p < 0.001) and the value of the interaction between intervention and time was also significant (p = 0.004). There were no statistically significant differences between the participants’ working memory in the different variables under study, except for the values of the Spatial location test in the experimental group that improved relative to the initial assessment (p = 0.034).Discussion: A virtual reality cognitive training intervention resulted in improvements in the processing speed measures, which were not found in the control group. Although we cannot make the same conclusions regarding working memory, these results suggest that the VR intervention resulted in progress in the experimental group, possibly influenced by the intervention, which should be verified in future studies with longer interventions.

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Using Hot and Cool Measures to Phenotype and Predict Functional Outcomes Across Dimensions of ADHD and Typical Development in Adolescents

Elahi,

Iosif,

Mukherjee

et al. 2023

Res Child Adolesc Psychopathol

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Multiple pathway models propose that attention deficit hyperactivity disorder (ADHD) arises from dysfunction in separate systems comprised of a "cool" or cognitive pathway versus a “hot” or emotional/reward pathway. Interactions between these pathways and the degree of maturation may further determine functional outcomes for adolescents ranging from those diagnosed with ADHD to typical development (TD). We used a latent profile analysis on rating scales and behavioral task performance assessing emotion, irritability, impulsivity, risk-taking, future orientation, and processing speed (PS) to identify subgroups of TD adolescents and adolescents with ADHD (N = 152) based on the hot and cool pathway model. We identified four classes: 1) High-Complex Challenges; 2) Moderate-Mixed Challenges; 3) Non-Emotive Impulsivity; and 4) High Regulation and Control. A multiple pathway model of ADHD is supported with classes differing in degree of emotional lability and irritability, types of impulsivity, and ability to use future consequences to modulate impulsivity and PS. The classes differed regarding functional behavior, with the High-Complex class demonstrating the most severe functional challenges in academic-related functioning. The Moderate-Mixed class also displayed significant functional challenges but with moderate emotional lability and irritability ratings. The Non-Emotive Impulsivity class exhibited low emotionality and low irritability, yet high impulsivity with limited negative functional consequences, and was composed of a mix of ADHD and TD adolescents. Differences between classes suggest ADHD symptomatology may represent both categorical and dimensional differences. Precision health interventions may be more effective in addressing the specific challenges associated with the classes rather than a one-size-fits-all approach to treating ADHD.

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Neural basis of working memory in ADHD: Load versus complexity

Cited by 14 publications

References 78 publications

Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

Neurophysiological measures and correlates of cognitive load in attention‐deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations

The effect of a virtual reality based intervention on processing speed and working memory in individuals with ADHD—A pilot-study

Using Hot and Cool Measures to Phenotype and Predict Functional Outcomes Across Dimensions of ADHD and Typical Development in Adolescents

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